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Speaker Abstracts
General Session I Thursday, February 15, 2018 | 8:15 AM – 12:30 PM
Plant phenotyping: overcoming the bottleneck by integrated approaches
Roland Pieruschka, PhD, Forschungszenrum Jülich
Forschungszentrum, Jülich, Germany
Quantitative analysis of structure and function of plants has become a major bottleneck
in research and applied use of plants. Approaches targeting relevant traits are needed to
quantitatively address key processes and understand the dynamic interactions between
genetic constitution, molecular and biochemical processes with physiological responses
leading to the development of phenotypes.
In this presentation, I will use case studies to demonstrate how plant phenotyping
infrastructure can be used to address relevant biological questions for accurate
measurement of biomass, structure and functional properties of plants across different
scales and developmental stages. For instance, I will present the use of automated systems
for the cultivation and imaging of model and crop species and demonstrate phenotyping
pipelines across scales under controlled and filed conditions. In the second part of the
presentation I would illustrate the role of plant phenotyping networks, summarize the
recent activities such as the EU funded project EPPN2020 that enables European
scientists to access plant phenotyping facilities across Europe and, the ESFRI listed
project EMPHASIS that aims at long- term sustainable development of the plant
phenotyping infrastructure in Europe. Finally, the International Plant Phenotyping
Network, a non-profit association integrates the plant phenotyping community as a global
communication hub.
From Phenotypes to Mechanisms: Approaching Root Growth Control Using
Systems Genetics
Wolfgang Busch, PhD, Salk Institute for Biological Studies
Salk Institute for Biological Studies
What is the basis for the profound variation of phenotypes and within a single species?
Uncovering the relevant genetic variants and the molecular mechanisms which these
variants affect, would have tremendous implications for a large number of application
ranging from breeding to precision medicine. Using the root system of the model plant
Arabidopsis thaliana, we have approached this problem using a systems genetics
approach that integrates high throughput phenotyping, genome wide association
mapping and functional genomic approaches. We have discovered multiple novel
mechanisms that underlie the natural variation of root traits and have explored the
relation of these gene variants and root traits with climate and soil parameters. Among
the most outstanding mechanisms is a signaling module of Leucine-Rich-Receptor-Like-
Kinases in which natural genetic variation determines root growth responses to low iron
levels. Interestingly, these genes are also involved in defense responses. Overall, our
work demonstrates that systems genetics approaches harnessing existing natural genetic
variation, phenomics as well as modern post-genome-era approaches, allow us to
understand genetic and molecular mechanisms that underlie phenotypic variation and
most likely contribute to local adaption.
Integrating sensor technology and ground platforms: Case studies in
proximal sensing and field phenomics in desert environments
Pedro Andrade-Sanchez, PhD, University of Arizona
University of Arizona
This presentation will provide a brief review of ground-based sensor platforms used for
plant trait characterization, with particular emphasis on systems applied to research
under the dry desert conditions of the US Southwest and irrigated agriculture. A
description of approaches for continuous monitoring of sensor platform position,
measurements of plant spectral and thermal response along with plant geometry will be
included in this talk. Plant canopy height characterization will be presented in more detail
as a case study. Canopy height can be interpreted as one axis of canopy volume, therefore
interpretation of electronic displacement sensor signals is an efficient way to characterize
plant geometry. Canopy height in sorghum is important because of many factors,
maximum plant height usually shows strong relations with net productivity. In cereals,
rapid changes in canopy height are potential indicators of panicle initiation and onset of
grain filling, although this association likely varies with photoperiod and genetics. Since
canopy height determines the working distance between sensors and the crop surface,
accurate measurement of heights is also of value for proximal sensing. Mechanical
actuation is an integral component of sensor platforms that allow adjustments in vertical
frame position. This way, the sensor height may be raised as the crop grows to maintain
a fixed working distance, and height data may be used as covariates in analyses of
proximal sensing datasets. Plant height also affects crop management, especially in
relation to lodging and mechanical harvest.
Fusion of multi-sensor imagery and machine learning for inspecting and
grading of agricultural products
Todd De Zwaan, PhD, LemnaTec Corporation
Solmaz Hajmohammadi – LemnaTec
Substantial improvements in plant breeding and crop management to feed a projected
population of 9-billion is one of this century’s grand global challenges. Early and accurate
detection and diagnosis of plant diseases, even before specific symptoms become visible
are a key factor in crop yield. This can be achieved by the development of high-resolution
systems equipped with multiple sensors measuring beyond the visible light spectrum.
This is a data intensive approach and demands analytical methods that can cope with the
resolution, size and complexity of the signals from these sensors. This approach also
needs high-throughput capabilities to measure more complex phenotypic information at
higher volumes in production environments.
In recent years, impressive results have been achieved in image detection and
classification that extended the market of computer vision applications in agriculture.
However, any nontrivial machine learning algorithm needs a high-quality dataset. A
result of the ever increasing development of sensors for rapid and non-destructive
assessment of plants is the ability to fuse the output of these sensors to create higher order
datasets. Multi-sensor fusion aims to integrate data collected at different temporal,
spectral and spatial scales to deliver more knowledge content than could be achieved by
each sensor independently.
Phenotyping occurs at laboratory, greenhouse, and field scales. Therefore, the demand
for platforms in each of these settings that have multi-sensor capabilities is high.
LemnaTec is addressing this demand with software and hardware systems that assess
phenotypes of plants and their organs from millimeter to meter scale in laboratory,
greenhouse and field settings. This talk will focus on the sensor fusion methodology in
different platforms using 2D and 3D datasets, and highlight applications of machine
learning tools for segmentation and quality monitoring using hyperspectral imaging.
Rapid gas exchange in the phenomic era
David Hanson, University of New Mexico
Joseph Stinziano – University of Western Ontario
Phenotyping for photosynthetic gas exchange parameters is limiting our ability to select
plants for enhanced photosynthetic carbon gain and to assess plant function in current
and future natural environments. This is due, in part, to the time required to generate
estimates of the maximum rate of ribulose-1,5-bisphosphate carboxylase oxygenase
(Rubisco) carboxylation, the maximal rate of electron transport, and Rubisco activation,
from the response of photosynthesis to the CO2 concentration inside leaf air spaces. To
relieve this bottleneck, we developed a method for rapid photosynthetic carbon
assimilation CO2 responses utilizing non-steady-state measurements of gas exchange.
Using high temporal resolution measurements under rapidly changing CO2
concentrations, we can collect traditional gas exchange parameters in around 2 minutes.
This is a small fraction of the time previously required for even the most advanced gas
exchange instrumentation. We present how we have applied this method to diurnal
changes in physiology as well as responses to light, CO2, and temperature.
Portable, scalable, high throughput geospatial analyses with Singularity
containers on cloud and high performance computing.
Tyson Swetnam, PhD, BIO5 Institute, University of Arizona
Reproducible science with geographic information systems (GIS) on cloud, high
throughput computing (HTC), and high performance computing (HPC) requires portable,
scalable, workflows as part of the Research Object. Here we present a method for running
free and open-source software for GIS; i.e. Geospatial Data Abstraction Library (GDAL),
Geographic Resources Analysis Support System (GRASS), and System for Automated
Geoscientific Analyses (SAGA), in tandem with a workflow management system,
Makeflow, on cloud and HPC using Singularity containers. Our example workflow
involves the computation of daily and monthly sum solar irradiation using an OpenMP
version of the GRASS r.sun algorithm. A single virtual machine (VM) masters the
workflow, with remote workers connected over Internet2 started on cloud, HTC, and/or
HPC platforms, all using the same Singularity container. The workflow is currently
deployed on the OpenTopography.org cyberinfrastructure, where users can select any
location on the terrestrial earth surface using national or global digital elevation model
(DEM) data to calculate global irradiation and daily hours of sunlight. Our workflow links
with OpenTopography via the Opal2 toolkit for wrapping this particular scientific
application as a Web service from a XSEDE Jetstream VM. The workers are launched on
demand on XSEDE Comet HPC and Open Science Grid HTC. Importantly, because the
workflow is containerized with Singularity, it can be re-deployed on any combination of
local desktop, cloud, or HTC / HPC by simply pulling the code from our GitHub repository
and following a few basic setup instructions. Containerized workflows such as ours that
take an open science approach, as part of the Research Object, will allow for future
reproducible geospatial science on cyberinfrastructure.
Quantifying Nanoscale Biomechanical Properties of the Plant Cuticular
Waxes
Farzad Hosseinali, Texas A&M University, Biological & Agricultural Engineering
Department
The potential applications of Atomic Force Microscope (AFM) in quantifying the
biomechanical properties of plants tissue and membranes, such as the cuticle of tomato
fruits, have been introduced before. However, previous studies on the application of the
AFM in the surface characterization of cotton fiber were mainly focused on the AFM
capabilities in producing high-resolution topography images of either fiber surface or its
cross–section. In fact, cotton fiber cells are covered with a thin cuticular
membrane. The cuticle is mostly made of lipids, alcohols, and fatty acids (collectively
called ‘cotton wax’). The waxy layer can be 10 to 300 nm thick and
imparts hydrophobicity to the fiber surface. The main objective of this study was to
characterize and compare the surface nanomechanical properties of cotton fibers using
various modes of the AFM. Surface topography and friction images of the fibers were
obtained with conventional contact mode. The nanomechanical property images, such
as adhesion and deformation, were obtained with Bruker’s newly developed
high-speed force-volume technique, PeakForce QNM®. The differences in nanoscale
friction, adhesion, and deformation signals can be attributed to fiber surface
hydrophobicity and stiffness, which in turn depend on fatty acids’ hydrocarbon
chain length, film viscosity, and the waxy layer thickness.
Phylogenetic signal in subgenus Rubus (bramble, blackberry) leaflet shape
using geometric morphometrics
Juniper Kiss, Aberystwyth University
Plant phenotypic plasticity and different ways of genetic recombination during clonal and
sexual reproduction make the identification of some plant species difficult. Although DNA
barcoding has revolutionised species identification, polyploidy, hybridisation and
apomixis pose challenges to this process. Subgenus Rubus (brambles, blackberries) is one
of the most taxonomically challenging groups of dicots and their morphology based
classification has not been entirely consistent with their molecular phylogeny. The
definition of bramble species is controversial and is often reliant on leaf and leaflet
characters. Here, we combined geometric morphometrics with molecular analysis.A total
of 230 leaves from 115 specimens were imaged from different environments (woodland,
sandy beach, saltmarsh, grassland) in the UK. We conducted a three-loci molecular
analysis using ITS(internal transcribed spacer) region of nrDNA and two cpDNA regions,
maturase K (matK) and trnL–trnF for 23 representative leaf samples. We analysed the
shape of five-foliate and three-foliate leaves using landmark-based image analysis. Using
Principal Component Analysis (PCA) and Canonical Variate Analysis (CVA), the leaflet
shapes clustered according to the different environments. Discrimination Analysis (DA)
also confirmed that most of the group mean shapes were highly significantly different (P
< 0.001) at different locations, while it was more obscure when analysed for differences
in between bramble series. Using squared-change parsimony, the molecular phylogeny of
the haplotypes was projected into the leaflet morphospace. Permutation tests suggested
the phylogenetic signal in leaflet arrangement morphology to be statistically significant
(P < 0.05). These results suggest that each haplotype has different shapes in different
environments, while the overall shape differences of haplotypes could be explained by
their phylogeny. We suggest a statistically robust approach to combine morphometric
analysis with molecular data to understand the variability of leaflet shape which could
affect the morphology-based classification of Rubus.
Novel Machine Vision Phenotyping of Maize NAM Plants Reveals
Modulation Effect by Priming Depending on the Cold Temperature
Gokhan Hacisalihoglu, PhD, Florida A&M University
Seedling emergence is an important factor for yield, particularly under challenging
planting conditions. In the US corn belt, maize is planted in early spring, as soon as soil
temperatures are permissive to germination. At that time, temperatures often drop below
normal, which can delay or even kill the seedling. Seed pre-treatments have been shown
to improve germination in cold conditions in crops such as rice and cabbage, but are
largely unpublished in maize. To assess the effects of pre-treatments on early maize cold
tolerance, twenty-seven inbred parents of maize Nested Association Mapping (NAM)
population were primed using a synthetic solid matrix and then tested for cold tolerance
using a soil-based emergence assay. Primed kernels were incubated at 10°C for 5 days,
and then transferred to 24°C for emergence. DSLR cameras were used to capture images
every 30 min to obtain emergence profiles of each seedling. Emergence time was
determined from the time-lapsed images and multiple measures including final
emergence percentage, time to 50% emergence, and emergence rate were extracted for
each genotype. The cold treatment reduced total emergence of several genotypes.
However, priming pre-treatment protected the sensitive genotypes allowing nearly full
emergence. We also used single-kernel near infrared reflectance spectroscopy to
determine seed density, weight, oil, protein, and starch for the kernels prior to planting.
By combining kernel characteristics and emergence time, we found small, but highly
significant correlations between the kernel and early seedling performance.
A real-time, non-invasive, low-cost monitoring system for plant
phenotyping under stress
Jaderson Armanhi, University of Campinas
Phenotypic data are essential to understanding plant responses to environmental
changes. Conventional instruments to assess plant physiological status are often invasive
or destructive, such as pressure chambers and tensiometers, or designed to provide a
single data point, such as the infrared gas analyzer (IRGA) and the porometer. Although
these methods are reliable, they do not provide continuous monitoring of plant response
to environmental stresses, which might result in losses of relevant information regarding
the true physiological status and plant adaptation mechanisms. Real-time phenotyping
technologies are usually costly, and most platforms are restricted to phenotyping
facilities. Therefore, the development of low-cost phenotyping options is exceptionally
convenient for small-scale studies and experimental setups under growth chamber and
greenhouse conditions. Here we propose a simple and real-time monitoring system for
the remote study of plant physiology using low-cost and easy-to-handle electronic
components. Our system provides the constant monitor of leaf temperature, vapor
pressure deficit (VPD), soil moisture, water loss, as well as the air temperature, relative
humidity and light intensity. An integrated RGB camera was used to record plant
response over time and a modified camera was used to capture near-infrared images for
NDVI measurements. All sensors and cameras are connected to a microcontroller
Raspberry Pi that receives and processes signals and images through custom and
automated scripts. Real-time data are sent to an online server that plots graphs and
creates time-lapse movies on a webpage. Sensors and methods are currently being
validated in experiments designed to evaluate drought stress response in maize. By
providing temporal high-resolution data and imaging, our small-scale system has the
potential to bring valuable information on plant phenotyping in a low-cost manner.
High throughput monitoring anthesis progression of field-grown maize
plants
Seyed Mirnezami, Graduate Research Assistant, Iowa State University
The tassel is the male organ of the maize plant. Sufficient pollen production is crucial for
the production of hybrid seed. Good seed set requires both sufficient daily production of
pollen, but also pollen shed on enough days to ensure a good “nick” with the receptivity
of female inbreds. Traditional approaches for phenotyping anthesis progression are time-
consuming, subjective, and labor intensive and are thus impractical for phenotyping large
populations in multiple environments. In this work, we utilize a high throughput
phenotyping approach that is based on extracting time-lapse information of anthesis
progress from digital cameras. The major challenge is identifying the region of the interest
(i.e. the location of tassels in the imaging window) in the acquired images. Camera drift,
different types of weather, including fog, rain, clouds, and sun and additionally, occlusion
of tassels by other tassels or leaves complicated this problem. We discuss various
approaches and associated challenges for object detection and localization under noisy
conditions. We illustrate a promising deep-learning approach to tassel recognition and
localization that is based on Region with Convolutional Neural Network (R-CNN). It is
able to reliably identify a diverse set of tassel morphologies. We subsequently extract
time-dependent tassel traits from these localized images.
Dimensionality: Curse or blessing?
David Houle, Florida State University
The ability to acquire phenome-level data is wonderful – hundred dimensional vectors of
data! Our limited human brains, however, think in just three or possibly four,
dimensions, and we like our stories simple. All that raises the question: Once you have
phenomic data, what do you do with it? Many biologists still study phenome level effects
as a very large set of one-to-one mappings between each trait and the experiment or SNP,
depending on the application. Part of the reason that biologists are often stuck in this
mode is the claimed “curse of dimensionality,” where measuring more and more on the
same number of subjects is supposed to become less and less useful. I suggest a taking a
geometric approach where the object of study is not the effects of the SNP or experiment
on each of the hundreds of phenotypes we might measure, but the total length of the effect
vector, and its direction in phenotype space. This mode of thinking follows naturally from
a fully multivariate analysis of the phenotype. A second frontier is the incorporation of
multivariate predictors, for example of a genome-full of polymorphism, or a history of
environmental influences on the phenome itself. For such applications, we need to
introduce biologically-motivated structure to the analysis, for example using
regularization. By choosing the analysis to match our questions, we can escape the curse
of dimensionality, and indeed turn dimensionality to a blessing.
Sensor Systems for Monitoring Horticultural Crops: Challenges and
Opportunities
Reza Ehsani, UC Merced
UC-Merced
Collecting site- or plant-specific data under field conditions has been a challenging and
costly task for researchers and growers. Growers need data for efficient use of crop inputs
and more efficient control of pests and diseases. Scientists need these data for selecting
the best plant in their breeding program or evaluating the effectiveness of different field
practices and treatments. Canopy size and density, growth rate, early detection of pests
and diseases, rootstock and tree age, root size, root density, yield estimation, and yield
monitoring are examples of data that can be used in a plant production system. In recent
years, there has been a significant progress in the area of Unmanned Aerial Vehicles
(UAVs), multi-band and hyperspectral cameras, wireless sensors, and low-power low-cost
electronic components, and data processing units. These advances resulted in better
sensor systems. This presentation will provide an overview of the sensor system
technologies for tree crops that are commercially available or being developed.
Microneedles as wearable sensors for monitoring plant stress
Philip Miller, Sandia National Labs
Kaitlyn Read – UNM; Dave Hanson – UNM; Ronen Polsky – Sandia National Labs;
Patrick Hudson – UNM
Microneedles are microscale devices primarily used for minimally invasive drug delivery
in humans however our group has utilized them as wearable electrochemical sensors for
health monitoring applicaitons. Recently, we've adapted these sensors for plant stress
monitoring in sorghum and shown that these devices are well tolerated and easily
interfaced with several plant tissue types (leaves, root crowns, and stalks) with little
immune reponse from the plant. Three sensors systems are being developed utilizing our
electromolding method. In this technique, metal microneedles are made from
electroplating into predefined molds that are made from replicating structures fabricated
with two-photon polymerization utilizing laser direct write. With this method arrays of
both hollow and solid microneedles are possible. Hollow microneedles are being adapted
for turgor pressure sensors and solid microneedles for impedance probes and as
metabolite sensors. Initial results show impedance measurements can tract plant drought
stress and recovery via monitoring impedances at low frequencies (0.1-10kHz) between
microneedles in sorghum leaves and a probe in the soil. Microneedle metabolite sensors
have been designed to detect glucose and arrays of microneede sensors allow for
multiplexed detection for spatial mapping. A portable system for remote data logging of
impedance, metabolites, and turgor pressure has begun greenhouse testing and initial
results indicate the system is capable of measuring impedance and environmental
conditions autonomously.
Challenges and opportunities of using satellite imagery to derive insights
for Precision Agriculture applications
Xiaoyuan Yang, PhD, The Climate Corporation
Driving efficiency in agricultural production depends on a number of parameters that are
highly variable in space and time. Digital tools for precision agriculture, targeted at
tailoring subfield decisions, require timely, accurate and scalable input to generate
insights. Remote sensing technology is a viable solution to achieve this goal. Various
public and private satellite platforms become available with the capability of acquiring
global observation daily or tracking field history for the past 30 years. Customized narrow
spectral bands can be designed for specific applications such as understanding nitrogen
or water limitations to yield. In addition, the unique value of active remote sensing, e.g.
radar and lidar, are being adopted. The Climate Corporation delivers farmer facing field
health imagery in Climate FieldViewTM. Here we will review the challenges and
opportunities of using satellite imagery to derive agronomic insights. Sensor calibration,
preprocessing, cloud/shadow detection, and atmospheric correction etc. are necessary to
provide high quality image input at scale. Selecting proper combinations of spatial,
temporal and spectral resolution to address a specific problem is critical as tradeoffs often
exist in image sources. Combining imagery with weather, soil, and farm management
practices data, image based information layers can be produced applying advanced data
science algorithms. These layers encapsulate the information for decision making, such
as crop monitoring, scouting, stress detection, management zoning and yield prediction.
Moreover, scalable storage/computation platform is in need for running analytics on
enormous amount of image data.
Concurrent I: Robotics Thursday, February 15, 2018 | 2:30 PM – 5:30 PM
Human-Robot Interaction for High Performing Teams in Field Applications
Brittany Duncan, PhD, University of Nebraska, Lincoln
University of Nebraska
This talk will discuss the role of human-robot interaction in field-based robot
deployments and be focused on three individual research areas: integration of robots into
high performing teams, improved teleoperation, and necessary autonomy for improved
team performance. Specific research questions that will be addressed include: 1) What
role does the use of aerial vehicles play in shared decision making with high performing
and potentially distributed teams? 2) How can interfaces and interactions amplify the
current reach of the end users? and 3) What adaptations are necessary within the
autonomy to augment user perceptions in field-based environments? This discussion will
be of interest to researchers and practitioners in agriculture and robotics communities,
as well as those in the fields of human factors, artificial intelligence, and the social
sciences.
Design and Evaluation of a Field-Based High-Throughput Phenotyping
Robot for Energy Sorghum
Sierra Young, Iowa State University
University of Illinois
This article describes the design and field evaluation of a low-cost, high-throughput
phenotyping robot for energy sorghum. High-throughput phenotyping approaches have
been used in isolated growth chambers or greenhouses, but there is a growing need for
field-based, precision agriculture techniques to measure large quantities of plants at high
spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile
robot was developed to collect phenotypic data for individual plants and tested on two
separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging
techniques determined plant height, and a depth sensor measured stem width near the
base of the plant. A data capture rate of one acre, bi-weekly, was demonstrated for
platform robustness consistent for various environmental conditions and crop yield
modeling needs, and formative human-robot interaction observations were made during
the field trials to address usability. This work is of interest to researchers and practitioners
advancing the field of plant breeding because it demonstrates a new phenotyping
platform that can measure individual plant architecture traits accurately (absolute
measurement error at 15% for plant height and 13% for stem width) over large areas at a
sub-daily frequency.
Small Vision Sensors for Phenomics
Sanjeev Koppal, PhD, University of Florida
University of Florida
Biological vision performs amazing visual tasks with negligible power consumption.
Insect eyes for example, allow for optical flow, obstacle avoidance, target tracking,
navigation and even object recognition using micro-watts of power. If robotic drones had
this kind of low power vision, we could imagine massive impact on agriculture and
phenomics. However, despite the fantastic strides in computer vision in recent years,
delivering such high-performance and real-time capability, within tiny power budgets, is
still a distant dream. The reason is that core computer vision algorithms usually follow a
predictable pattern: large amounts of high-resolution imagery and video are combined
with massive amounts of computation. While this achieves spectacular results in many
domains, a new approach is required for the coming wave of next generation miniature
devices. These are micro and nano-scale devices, with feature sizes less than 1mm, that
will soon impact fields as diverse as geographic and environment sensing, agricultural
control and monitoring, energy usage and crop health. This talk is about our work in
solving the core problems that will enable computer vision on miniature platforms.
Allowing these small devices to reliably sense their surroundings has the potential for a
major transformation in phenomics and related fields.
PlantCV Tools for Hyperspectral Imaging of Abiotic Stress
Malia Gehan, PhD, Donald Danforth Plant Science Center
To tackle the challenge of producing more food and fuel with fewer inputs a variety of
strategies to improve and sustain crop yields will need to be explored. These strategies
may include: mining natural variation of wild crop relatives to breed crops that require
less water; increasing crop temperature tolerance to expand the geographical range in
which they grow; and altering the architecture of crops so they can maintain productivity
while being grown more densely. These research objectives can be achieved with a variety
of methodologies, but they will require both high-throughput DNA sequencing and
phenotyping technologies. A major bottleneck in plant science is the ability to efficiently
and non-destructively quantify plant traits (phenotypes) through time. PlantCV
(http://plantcv.danforthcenter.org/) is an open-source and open development suite of
image processing and analysis tools that could initially analyze images from visible, near-
infrared, and fluorescent cameras. Here we present new PlantCV analysis tools associated
with the development of a hyperspectral and 3D imaging platform aimed at the
identification of early abiotic stress response.
Phenotyping tree shape in the field using computer vision and robotics
Amy Tabb, PhD, USDA-ARS-AFRS
United States Department of Agriculture – Agricultural Research Service
Phenotyping of tree shape is a challenging problem, not least of which because the
traditional metrics of tree shape – height, width, branch number, branch angle, branch
diameter, and branch length, may not be particularly characteristic of the structural
differences that are evident to humans between phenotypes. We describe ongoing work
to develop a robot vision system that captures the above metrics of fruit tree shape
autonomously and accurately, as well as complete tree reconstructions for use in novel
shape descriptors. I will demonstrate how the system operates in field settings, and
describe its constraints and possible applicability to other species.
Electrical Capacitance Tomography (ECT) to Monitor Root Health and
Development and Possible Application in Phenotyping
Daniel Sabo, PhD, Georgia Tech Research Institute
Ga Tech Research Inst
It is becoming increasingly important in plant phenotyping to have an understanding of
root development due to its importance to the health, development, and production
quality of plants. For breeders, it is important to develop cultivars with desired rooting
traits that contribute to resource use efficiency and improved yield. On the other hand,
information about root health and development would provide needed insight into plant
development and water/nutrient requirements. This means there is a universal need for
a new, nondestructive, and in situ method that monitors root health and development.
Electrical capacitance tomography (ECT) is a nondestructive technique that allows for
this desired root monitoring. We have shown that relative ECT measurements are able to
provide information for root development, insight into speed of root growth, and the
ability to distinguish healthy developing roots from stunted and dying roots,
nondestructively. ECT was also used for presymptomatic detection in bell pepper plants
and various stress effects on the roots. ECT has the ability to provide much needed
information on root health, speed of root growth, stress effects on rooting properties, and
root mass development, making it a desirable sensing technique for plant phenotyping.
Bracing for Impact: The role of aerial roots in plant stability
Erin Sparks, University of Delaware
Damage to plants that prevents them from staying upright, called lodging, can have
a significant impact on cereal crop yield. While there is a large emphasis on
reducing lodging, we understand little about how plants achieve stability. In maize
plants, aerial roots that emerge from the stem above the soil, called brace roots,
are proposed to play an important role in structural stability. Yet how brace roots
develop, integrate environmental cues and contribute to plant stability remains a
poorly understood area of plant biology. Research in our lab focuses on questions
regarding the development and function of maize brace roots. Specifically, we are
taking a structural engineering approach to define the contribution of brace roots
to plant stability. From structural engineering, we know that there are two key
features to building stable structures: the arrangement of the building materials
and the mechanical properties of the building materials. To extrapolate these
features into plants, we have developed a field-based crawling robot for brace root
phenotyping to define the arrangement of building materials. In addition, we are
subjecting brace roots to tension and compression testing to define the mechanical
properties of the building materials. This information is being integrated into structural
engineering models to determine the contribution of brace roots to plant stability. These
experiments are among the first to define the diversity of brace root architecture and
mechanical properties in maize, which is critical to understanding the significance of
these specialized roots in plant stability.
Concurrent II: New Sensors Thursday, February 11, 2017 | 2:30 PM – 5:30 PM
Spatial and spectral data for improved hyperspectral phenotyping
James Janni, PhD, DuPont Pioneer
DuPont Pioneer
Phenotypes have been characterized using both the spectral and spatial information
provided by Pioneer's automated hyperspectral imaging towers. A stressed phenotype
and leaf nitrogen estimations will be used to demonstrate the sensitivity for high
throughput plant characterization. Spatial variation of spectral response will be explored
for increased precision. Spectral indices and the inversion of the PROSPECT model will
be included.
Computer vision and hyperspectral approaches to document temperature
stress responses in maize seedlings
Tara Enders, University of Minnesota
Susan St Dennis – University of Minnesota; Nathan Miller – University of Wisconsin;
Liz Sampson – University of Minnesota; Edgar Spalding – University of Wisconsin;
Nathan Springer – University of Minnesota; Cory Hirsch – University of Minnesota
Yields of maize may be reduced substantially within the next century due to global climate
change. Understanding how maize varieties respond to temperature extremes will be
instrumental in developing varieties that can withstand future abiotic stresses while still
producing high yield. We are documenting the variation of morphological traits, color,
and hyperspectral signals in maize seedlings in response to abiotic stresses in multiple
maize genotypes over time. Morphological measurements, such as plant height, width,
and area, can help characterize the impact of stresses on growth rates. Color data from
RGB images allows for quantification of physiological changes to stress, such as leaf
necrosis, which varies substantially among maize genotypes. Hyperspectral data may
capture valuable information about how genotypes respond to stress that is unable to be
captured using RGB imaging and could provide early detection of stress responses prior
to other manifestations. Documenting multiple traits across genotypes and growth
conditions will uncover the dynamics of maize responses to changing temperatures and
allow for the discovery of genomic loci that could provide improved tolerance.
Tissue specific electrical impedance as a potential screening tool
Kaitlyn Read, University of New Mexico
Patrick Hudson – University of New Mexico; Philip Miller – Sandia National
Laboratories; David Hanson – University of New Mexico
Electrical Impedance Spectroscopy (EIS) is a commonly used noninvasive method to
predict root dimensions, tissue damages, and other physiological parameters. These
methods typically rely on measuring through an electrically variable medium (ie soil,
hydroponic fluid, and epidermal layers), or destructively removing part of the plant. Here
we demonstrate the utility of microneedles to apply EIS methods to specific organs and
tissues in planta. Microneedles were placed on both the adaxial and abaxial surfaces of a
sorghum (Sorghum bicolor) leaf midrib, to measure water storage and water transport
tissues, respectively. An 18-gauge needle was placed 1 cm below the leaf-stalk junction to
function as the signal receiver for both microneedle placements. A handheld LCR meter
supplied a voltage of 0.6V AC, and measured impedance and phase angle at four different
frequencies. Microneedle impedance values were compared to planar metal transducers
as a control, which didn’t penetrate the plant tissue, and impedance values across all
frequencies tested were significantly lower with the microneedle devices. After in planta
EIS measurements were concluded, a fully expanded leaf was removed. Water storage
and water transport tissues were dissected, and EIS measurements were repeated in the
isolated tissues. Impedance was significantly lower in water transport tissue compared to
water storage tissue, both in planta and in isolation. One week after in planta
measurements, leaves showed no adverse response to microneedle applications, other
than superficial callose deposition at the injection site. Our results show that microneedle
EIS can distinguish specific tissues in a non-destructive fashion, and offer a novel
opportunity for high resolution, real-time plant monitoring.
Phenomics of stomata and water use efficiency in C4 species
Andrew Leakey, PhD, University of Illinois at Urbana-Champaign
Andrew D.B. Leakey1*, John Ferguson1, Nathan Miller2, Jiayang Xie1, Charles Pignon1,
Gorka Erice1, Timothy Wertin1, Nicole Choquette1, Maximilian Feldman3, Funda
Ogut4, Parthiban Prakash1, Peter Schmuker1, Anna Dmitrievna1, Dylan Allen1,
Elizabeth A. Ains
University of Illinois at Urbana-Champaign
Water use efficiency (WUE), which is physiologically distinct from drought tolerance, is a
key target for improving crop productivity, resilience and sustainability. This is because
water availability is the primary limitation to crop yield globally and irrigation uses the
largest fraction of our limited and diminishing freshwater supply. The exchange of water
and CO2 between a leaf and the atmosphere is regulated by the aperture and pattern of
stomata. Mechanistic modeling indicates that stomatal conductance could be reduced or
stomatal movements accelerated to improve water use efficiency in important C4 crops
such maize, sorghum and sugar cane. While molecular genetics has revealed much about
the genes regulating stomatal patterning and kinetics in Arabidopsis, knowledge of the
genetic and physiological control of WUE by stomatal traits in C4 crops is still poor.
Understanding of natural diversity in stomatal traits is limited by the lack of high-
throughput phenotyping methods. To this end two novel phenotyping platforms were
developed. First, a rapid method to assess stomatal patterning in three model C4 species
grown in the field – maize, sorghum and setaria has been implemented. Here the leaf
surface is scanned in less than two minutes with a modified confocal microscope,
generating a quantitative measurement of a patch of the leaf surface. An algorithm was
designed to automatically detect stomata in 10,000s of these images via a training of a
pattern-recognition neural network approach. Second, a thermal imaging capture
strategy, to rapidly screen the kinetics of stomatal closure in response to light has been
developed. We are gaining insight on the underlying genetics governing stomatal stomatal
patterning through quantitative trait loci and genome wide association studies in addition
to phenotypic evaluations of sorghum with transgenically modified expression of stomatal
patterning genes. These multifaceted approaches are complemented by a recently
established field facility for comprehensive evaluation of leaf, root and canopy WUE traits
under Midwest growing conditions.
Phenomics at Scale: Driving Advances in Plant Breeding with Insights from
Diverse Sensor Platforms
Nadia Shakoor, PhD, Donald Danforth Plant Science Center
Donald Danforth Plant Science Center
With the rapid advancement and implementation of robust and high quality genetic and
genomic technologies, the functional analysis of new genomes is currently limited by the
quality and speed of high throughput phenotyping. Ongoing advances in genomics and
high throughput phenotyping creates multiple layers of valuable information that can be
exploited to rapidly advance breeding. In recent years, major contributions from
government and private organizations have been invested in the creation and use of high
throughput tools to speed the development and deployment of phenotyping and breeding
technologies to benefit researchers and farmers. The TERRA-REF program and the
Sorghum Genomics Toolbox, funded by the Department of Energy’s ARPA-E program
and the Bill and Melinda Gates foundation, are employing cutting-edge technologies to
sequence and analyze crop genomes, along with deploying various scales of imaging
platforms (e.g, UAS, tractor-based and indoor and outdoor field scanner systems) to
capture millions of phenotypic observations across growing seasons and diverse
environments to accelerate crop breeding efforts by connecting those phenotypes to
genotypes.
For example, breeding for cold temperature adaptability is vital for the successful
cultivation of bioenergy sorghum at higher latitudes and elevations, and for early season
planting to extend the growing season. Through the TERRA-REF project, we have
successfully resequenced 400 bioenergy sorghum lines and carried out high throughput
phenotyping to identify candidate genes and alleles that enhance biomass accumulation
of sorghum grown under early season cold stress. Genome wide association studies
(GWAS) of temporal growth data identified potential genes and time specific quantitative
trait loci (QTL) controlling response to early cold stress, permitting an investigation into
the temporal genetic basis of cold stress response at different stages of plant development.
Heliaphen, an outdoor high-throughput phenotyping platform designed to
integrate genetics and crop modeling
Florie Gosseau, LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan, France
Florie Gosseau – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan,
France; Nicolas Blanchet – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-
Tolosan, France; Louise Gody – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-
Tolosan, France; P
Heliaphen is an outdoor high-throughput phenotyping platform allowing automated
management of growth conditions and monitoring of plants during the whole plant cycle.
A robot moving between plants growing in 15L pots monitors plant water balance and
phenotypes plant or leaf morphology, from which we can compute more complex traits
such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit.
Here, we illustrate the platform capacities on two practical cases: a genetic association
study for yield-related traits and a simulation study, where we use measured traits as
inputs for a crop simulation model. For the genetic study, classical measurements of
thousand-kernel weight (TKW) were done under water stress and control condition
managed automatically on a sunflower bi-parental population. The association study on
TKW in interaction with hydric stress highlighted five genetic markers with one near to a
gene differentially expressed in drought conditions from a previous experiment. For the
simulation study, we used the SUNFLO crop growth model to assess the impact of two
traits measured in the platform (LE and TR) on crop yield in a large population of
environments. We conducted simulations in 42 contrasted locations across Europe and
21 years of climate data. We identified ideotypes (i.e. genotypes with specific traits values)
that are more adapted to specific growing conditions, defined by the pattern of abiotic
stress occurring in these type of environments.
This study exemplifies how phenotyping platforms can help with the identification of the
genetic architecture of complex response traits and the estimation of eco-physiological
model parameters in order to define ideotypes adapted to different environmental
conditions.
Beyond Orthomosaics: Multi-Image Spectral Analysis of Agricultural UAV
Imagery
Travis Gray, University of Saskatchewan
William van der Kamp – University of Saskatchewan; Travis Gray – University of
Saskatchewan; Steve Shirtliffe – University of Saskatchewan; Hema Duddu – University
of Saskatchewan; Kevin Stanley – University of Saskatchewan; Ian Stavness –
University of Saskatchewan
Imagery from Unmanned Aerial Vehicles (UAVs) is frequently used for crop assessment
and phenotyping in agricultural research fields. In particular, spectral indices, such as
NDVI, are commonly employed to estimate traits such as the health, growth stage, and
biomass in crops. In virtually all such studies, many overlapping images from a UAV flight
are first stitched into a single orthomosaic image, and then spectral indices are derived
from the orthomosaic. But this method necessarily discards (or aggregates) much of the
original pixel information. For example, a single plot may appear in 10 or more individual
images from the UAV, but appear only once in the final orthomosaic. In this paper, we
show that an index value extracted from individual calibrated images of the same plot can
deviate by 10% or more from the index value of the corresponding orthomosaic segment.
This could have important consequences for fine-grained comparison of indices. To
address this problem, we propose alternative approaches to estimating indices, each of
which more directly incorporates all of the individual UAV images. We evaluate these
approaches, and compare them with the orthomosaic approach, by analysing weekly
index values from plant breeding experiments for lentil, wheat, and canola crops in
comparison to relevant manually-measured phenotypes.
Technology Session Thursday, February 15, 2018 | 5:30 PM – 7:10 PM
Tackling the physiological phenotyping bottleneck with low-cost, enhanced-
throughput, do-it-yourself gas exchange and ceptometry
William Salter, , School of Life and Environmental Sciences, Sydney Institute of
Agriculture, The University of Sydney
William Salter – The University of Sydney; Matthew Gilbert – University of California,
Davis; Andrew Merchant – The University of Sydney; Thomas Buckley – University of
California, Davis
High throughput phenotyping platforms (HTPPs) are increasingly adopted in plant
breeding research due to developments in sensor technology, unmanned aeronautics and
computing infrastructure. Most of these platforms rely on indirect measurement
techniques therefore some physiological traits may be inaccurately estimated whilst
others cannot be estimated at all. Unfortunately, existing methods of directly measuring
plant physiological traits, such as photosynthetic capacity (Amax), have low throughput
and can be prohibitively expensive, creating a bottleneck in the breeding pipeline. We
have addressed this issue by developing new low-cost enhanced-throughput phenotyping
tools to directly measure physiological traits of wheat (Triticum aestivum). Our eight-
chamber multiplexed gas exchange system, OCTOflux, can directly measure Amax with
5-10 times the throughput of conventional instruments, whilst our handmade
ceptometers, PARbars, allow us to monitor the canopy light environment of many plots
simultaneously and continuously across a diurnal cycle. By custom-building and
optimizing these systems for throughput we have kept costs to a minimum, with
OCTOflux costing roughly half that of commercially available single-chamber gas
exchange systems and PARbars costing approximately 95% less than commercial
ceptometers. We recently used these tools to identify variation in the distribution of Amax
relative to light availability in 160 diverse wheat genotypes grown in the field. In a two-
week measurement campaign we measured Amax in over 1300 leaves with OCTOflux and
phenotyped the diurnal light environment of 418 plots using 68 PARbars. These tools
could be readily modified for use with any plant functional type and also be useful in
validating emerging HTPPs that rely on remotely sensed data to estimate photosynthetic
parameters.
Application of Crop Phenotyping to Computation Agronomy at CiBO
Jasenka French, PhD, Cibo Technologies
Cibo Technologies
At CiBO Technologies, we use software and science to solve problems across the whole
agriculture value chain. We are addressing challenges of sustainability, climate change,
and food security by unifying big data and advanced analytics with a fundamental
understanding of the complexities of agriculture.
Crop phenotyping and environment characterization via imaging technologies is an
important part of enabling massive simulations and inference problems that CiBO is
building the framework to address. We will discuss CiBO’s approach and some challenges
in this demanding quest.
CT image-based Segmentation and Reconstruction of Root Systems by
Machine Learning and Computational Methods
Zheng Xu, PhD, University of Nebraska-Lincoln
Camilo Valdes – Florida International University; Stefan Gerth – Fraunhofer Institute
for Integrated Circuits IIS; Jennifer Clarke, Sleep – University of Nebraska-Lincoln
Computed Tomography (CT) scanning technologies have been widely used in many
scientific fields, especially in medicine and materials research. A lot of progress has been
made in agronomic research thanks to CT technology. CT image-based phenotyping
methods enable high-throughput and non-destructive measuring and inference of root
systems, which makes downstream studies of complex mechanisms of plants during
growth feasible. An impressive amount of plant CT scanning data has been collected, but
how to analyze these data efficiently and accurately remains a challenge.
We present new computational and machine learning methods for better segmentation
and reconstruction of root systems from 3D CT scanning data. We propose new
approaches within the category of voxel thresholding methods. Considering special
characteristics of root systems, we propose our methods based on two new local-feature
statistics, i.e., proportion and weighted proportion. We found that methods based on our
two new statistics can calibrate root system magnitudes faster than traditional vessel-
based approaches while preserve similar levels of performance. In addition, we propose
and evaluate machine learning approaches in root-system segmentation and
reconstruction from CT-images, in particular simulation-assisted machine learning
approaches. We illustrate and compare different approaches using both simulated and
real CT scanning data from Fraunhofer Institute for Integrated Circuits IIS.
PlantEye F500: combine 3D and multispectral information in one sensor
Grégoire Hummel, PhD, CEO, Phenospex B.V.
PlantEye is a high-resolution 3D laser scanner that computes a robust and validated set
of morphological plant parameters fully automatically. A core feature of PlantEye is that
it can be operated in full sunlight without any restrictions - crucial for plant phenotyping
under field conditions or if you follow a “sensor-to-plant-concept”. Phenospex has now
developed a new sensor generation, which combines the actual features of PlantEye on
the fly with a 4-channel multispectral camera in the range between 400 – 900nm. This
unique hardware-based sensor fusion concept allows us to deliver spectral information
for each data point of the plant in X, Y, Z-direction and we can compute parameters like
NDVI, color index and many other vegetation indices. This new sensor generation opens
a wide range of new possibilities in plant phenotyping and increases its efficiency.
RhizoVision-Crown: An open hardware and software phenotyping platform
for root crowns using a backlight, a machine vision camera, and a new C++
image analysis program
Larry York, PhD, Noble Research Institute
Anand Seethepalli – Noble Research Institute; Haichao Guo – Noble Research Institute;
Marcus Griffiths – Noble Research Institute
Root crown phenotyping, or shovelomics, has become increasingly popular to evaluate
the root systems of crops from the field. Generally, a root crown is excavated and soil is
removed. Earlier methods used a combination of rating and manual measurements such
as number of axial roots, angles of axial roots, lateral root branching density and length,
and diameters. However, imaging followed by image analysis has become increasingly
popular because the techniques are faster and more precise. However, no standard for
imaging has emerged and reproducibility of imaging conditions is difficult, which leads
to images that may be hard to segment and analyze. Here, we describe a new phenotyping
platform that combines custom hardware and software to optimize the imaging of root
crowns and allows fast image analysis with software that is easy to install. The
RhizoVison-Crown hardware platform consists of an extruded aluminum tubular
structure with a 2 ft LED ceiling light panel on one side and a monochrome machine
vision camera on the other. Root crowns are affixed to top panels using a clip and inserted
into a fixed position for imaging, which makes switching crowns fast and ergonomic. The
hardware can be constructed for < $1000. Image acquisition settings are set so the output
of the camera is a quasi-segmented black root crown on a white background that is easily
fully segmented using simple thresholding. A barcode scanner is used to trigger the image
acquisition and the images are stored with the encoded identity using custom software.
The image analysis software is based on OpenCV and written in C++. Extracted features
include root length, area, convex hull, width, depth, diameters, and angles. Examples of
its operation and use in several species will be discussed. This relatively inexpensive and
reproducible system may allow more opportunities for researchers to conduct root
research.
Image Analysis using CyVerse
Blake Joyce, PhD, CyVerse, BIO5 Institute, University of Arizona
Blake Joyce – CyVerse, BIO5, University of Arizona
Phenotyping is poised to surpass genotyping as the next 'big data' challenge. UAVs,
tractors, and remote senors are capable of producing terabytes of data daily for individual
hectares of land. Now that data acquisition is becoming easier, analysis at scale will be the
next bottleneck. CyVerse can provide data storage and management to research teams,
specialized image analysis through CyVerse BisQue, and on-demand cloud computing
through CyVerse Atmosphere. We'll give an overview of image analysis using CyVerse.
Additionally, ongoing development will be discussed for phenotyping-related analyses
like machine learning image feature recognition, analyses running on GPUs, and
integration with geographical information system (GIS) analyses.
Phenotyping for Plant Breeding using 3D Sensors and a Generic 3D Leaf
Model
Oliver Scholz, Fraunhofer Development Center X-Ray Technology
Franz Uhrmann – Fraunhofer Development Center X-Ray Technology; Katharina Pieger
– Fraunhofer Development Center X-Ray Technology; Dominik Penk – University of
Erlangen-Nuernberg; Guenther Greiner, Prof. – University of Erlangen-Nuernberg
We present a setup to objectively assess sugar beet plant traits using multiview
stereoscopy with color cameras with the goal of generating relevant phenotyping
parameters to aid the breeder. The setup was tested in a greenhouse environment as well
as in the field. The assessment is performed using a generic leaf model adapted to the
specific requirements of the sugar beet breeder. The evaluation yields global plant
parameter including plant height, leaf area, leaf count, etc. as well as per-leaf parameters
for each leaf of the plant. The leaf model is based on semantic geometric parameters,
which can directly be interpreted by the breeder.
High Throughput Photosynthesis Characterization of C3 Plants
James Bunce, PP Systems
Dr. James Bunce, Ph.D. in Plant Physiology – PP Systems; Andrew Lintz, B.S. in
Mechanical Engineering – PP Systems
Single point measurements of leaf gas exchange provide basically only the parameters net
photosynthesis, stomatal conductance, and instantaneous leaf water use efficiency. From
analysis of assimilation rate vs. internal CO2 concentrations (A vs. Ci) curves, four or five
additional leaf parameters are obtained for plants with C3 carbon metabolism, which
allow estimation of photosynthesis over a range of conditions. However, determining A
vs. Ci curves conventionally requires at least 20 minutes per leaf, compared with about 2
minutes for single point measurements, which greatly limits through-put. PP Systems
has developed a method of linearly ramping CO2 rapidly in their CIRAS-3 Portable
Photosynthesis System, which provides a complete A vs. Ci curve in 5 minutes per leaf.
Two initial steps are required: storing the changes in analyzer sensitivity with background
CO2, and collecting data from ramping of CO2 with an empty chamber. These two steps
need only be done once per day. The 5 minute total measurement period per leaf includes
a 2 minute initial equilibration period followed by 3 minutes of ramping up of CO2
concentration until the rate of change of A with CO2 becomes small, i.e. until CO2
becomes nearly saturating to A. With the CIRAS-3 system post-processing of the gas
exchange data is very simple: the apparent “A” of the empty chamber is subtracted from
the “A” value obtained with a leaf in the chamber at each time point of the CO2 ramping
period. This provides the actual A value at each time point, and the Ci is obtained from
this actual A, stomatal conductance, and external CO2 as in the conventional calculation
of Ci. Because of the rapid change in CO2, we have seen no significant change in stomatal
conductance during the CO2 ramps.
In situ phenotyping of root system architecture
Eric Rogers, Doctor of Philosophy, Hi Fidelity Genetics
Root system architecture (RSA) plays a pivotal role in plant fitness and yield yet
remains an untapped resource for crop development. Breeders have primarily
ignored root phenotyping due to an absence of suitable in situ phenotyping
methods. We have developed a device that can detect root growth in real time in
the field, called a RootTracker. The RootTracker runs on battery power and sends
readings wirelessly to a nearby microcomputer that stores and uploads the data to
the cloud for analysis. In its current form factor, the RootTracker can identify
individual roots, and can distinguish growth angles and growth rates. We have
demonstrated long-term deployment of RootTrackers in three maize field sites.
These trials showed we could distinguish root growth rates between two different
maize cultivars. We are currently working to refine our design and add additional
soil property sensors that would be useful to farmers and breeders.
Leveraging Sensors, Probes and Drones to Enable Data Driven Decisions
for Growers
Bruce Schnicker, The Climate Corporation
Technologies and tools that agricultural scientists only dreamed of accessing even as
recently as 10 years ago are now a standard component of our agricultural data acquisition
platform. Sensors, probes, drones, cameras, and a plethora of other technologies are
routinely used at The Climate Corporation to accelerate the development of value added
models to our digital platform. These technologies are leveraged across multiple testing
formats, including Research & Development, Commercial, Growers, and our own
internally managed research farms. Data and insights from soil, weather, equipment, and
the plants themselves are required to enable development and deployment of predictive
models for agricultural use. Successful utilization of these data layers results in products
that enable growers to make data driven decisions to enhance their performance and
profitability.
General Session II Friday, February 16, 2018 | 8:30 AM – 12:30 PM
Automation and robotics for high-throughput phenotyping and precision
horticulture and agriculture
Rick Zedde, Wageningen University & Research
Wageningen University
Automated plant phenotyping offers plant scientists, breeders and growers a powerful
tool to gather vast amounts of growth data to understand and optimise plant performance
and productivity. For effective use in industry these tools need to be fast, accurate and
objective. Robots developed for phenotyping purposes can be translated to horticultural
production locations and solutions for precision agriculture might benefit phenotyping
purposes. This cross-disciplinary interaction launched as a catalyst a range of novel
technological developments.
Integrating truly transdisciplinary approaches in forming a novel pipeline
between questions and solutions addressing crop stress.
Diane Rowland, Doctor of Philosophy, University of Florida
D.L. Rowland1, A. Zare2, Y. Tseng1, S. Zou2, X. Guo2, H. Sheng2, B. Zurweller1, and R.
Gloaguen1
University of Florida
Critical breakthroughs in crop stress will require transdisciplinary approaches.
Transdisciplinarity follows the concept of “Convergent Research” from the National
Science Foundation: utilizing deep integration across disciplines by immersion in cross
disciplinary language, techniques, strategies, and constant team interaction – efforts all
aimed at one compelling problem. This contrasts “interdisciplinary” research, where
groups remain in disciplinary silos, approaching research objectives in isolation with
team members meeting periodically to simply report ongoing results. Achieving
transdisciplinarity takes a period of integration and education among disparate
disciplines, and confronting existing research paradigms. Research groups within the
Center for Stress Resilient Agriculture and the Machine Learning and Sensing Laboratory
have formed a transdisciplinary group aimed at solving critical problems related to crop
stress. Efforts include group study of primary literature in both crop science and machine
learning and application of these concepts in an integrated approach to address specific
research challenges. We will present two specific examples where this approach has
brought success in addressing deeper problems not able to be solved through one
disciplinary tactic alone. The first is the utilization of hyperspectral images for
determining peanut maturity by sensing mesocarp color indicative of pod development.
This approach can address risk for aflatoxin, a mycotoxin known to be responsible for
significant levels of liver cancer across the globe, and which is produced when the crop
experiences drought stress. The second is the utilization of root modeling and stochastic
optimization approaches for determining the relationship between root architecture and
root function – an approach that challenges the current paradigm about rooting depth
and branching serving as adequate surrogates for predicting root function by directly
testing the validity of this assumption. These case study approaches show the success of
taking a truly transdisciplinary approach and the significant advances that can be
achieved when doing so.
CIAT Phenomics Platform: Aiming at improving Eco-efficiency of crops in
the changing global climate
Michael Selvaraj, PHD, International Center for Tropical Agriculture
Nowadays analyzing the phenotype is frequently slower and more expensive than
genomics due to the difficulties of measuring plant behavior at different levels and under
different environements. Thus phenotyping becomes the limiting factor for plant biology
and crop improvement. Our knowledge on the link between genotype and phenotype is
currently hampered by insufficient capacity of the plant science community to analyze the
existing genetic resources for their interaction with the environment. Advances in
developing plant phenotyping methods and tools are therefore essential for success in
characterizing shoot and root phenes to design next generation crops and forages as key
components for climate-smart or eco-efficient agriculture. Constraints in field
phenotyping capability limit our ability to dissect the genetics of quantitative traits,
particularly those related to yield, biotic and abiotic stress tolerance. The development of
effective field-based phenomics platforms remains to be a bottleneck for future advances
in genetic gain for yield and nutritional quality. However, progress in remote sensing
technology and high-performance computing are paving the way. The CIAT field
Phenomics platform at CIAT-HQ is a state-of-the-art, high-tech facility comprised of
automated rainout shelters (for drought screening) and low nitrogen field plots (for
Nitrogen use efficiency screening) integrated with multi spectral imaging and Terrestrial
Laser Scanning (TLS) system mounted on phenotowers, roof of rainout shelters and
unmanned aerial vehicles (UAV). This automated, high-throughput platform allows
repeated non-destructive image capture and multi-parametric analysis of small to
medium sized field plots at multiple time points. CIAT phenotyping platform is also
developing the capacity to estimate root yield in cassava using Ground Penetrating Radar
(GPR) technology. The mounting of multi-spectral camera to a drone (UAV) can
potentially harness the full capability of proximal sensing in a reliable, flexible, and
efficient system that operates spatially at small to bigger plots. Combining this approach
with environmental characterization as (Climate, soil and management status of the
crop), with GPS positioning to spatially locate the proximal sensing data and with
automated image analysis thus appears capable of delivering a robust field based
phenomics platform.
Quantitative imaging and dynamic models of plant stem cells
Ross Sozzani, PhD, NCSU
R. Sozzani1, M. A. de Luis Balaguer1; N. Clark1, 2; A. Fisher1;
1. Department of Plant and Microbial Biology, North Carolina State University;
2. Biomathematics Graduate Program, North Carolina State University;
The stem cells in the tip of the Arabidopsis root form all the root tissues by undergoing
rounds of coordinated cell division while maintaining their undifferentiated state. A
better understanding of the transcription factors that maintain the stem cells, and control
each stem cell’s identity, would give us more insight into how the growth and development
of the root is initiated. While a number of transcription factors involved in root stem cell
maintenance have been described, a comprehensive view of the transcriptional signature
of the stem cells is lacking. We have generated a model of the transcriptional mechanisms
underlying the identity and maintenance of the Arabidopsis root stem cells that links
known and newly predicted factors involved in these processes. We have gained
quantitative insights into these key factors by accurately measure molecular dynamics,
such as intercellular trafficking and protein-protein interactions, which allowed us to
generate models that capture the behavior of the system. This model led to a map of
genetic interactions that orchestrate the transcriptional regulation of stem cell
maintenance.
Plant Root Quantitative Analysis
Fuqi Liao, MA, The Noble Research Institute
Root quantitative analysis has become more and more important in plant research.
Currently, many biologists manually measure the root length with help of software (for
example, WinRhizo), but it is limited to small numbers of roots and costs a lot of time.
Many research projects (for example, GWAS) need to quantify a large number of root, and
require software to detect and process root images automatically and finish analysis in
short time. In many situations, precise measurements could be made by software, instead
of manual analysis. Here, we report development of a series of software, which automated
root image analysis with parallel computing on High Performance Computing (HPC)
cluster. The software have been applied to thousands of images of Arabidopsis and
Medicago truncatula roots. With many parameters, which quantifying the roots, the
automated feature of the software allows analysis of thousands of root samples within a
short period of time. The software can detect nearly 90% of the root hairs, whose length
is less than one millimeter, and measures the root hair lengths. For roots, which were
grown for two weeks in the lab, the software can detect tips of lateral roots, and analyze
the lengths of main root, and multi-layers of lateral roots. The software also quantifies the
roots from the field with many parameters, including root area, root angle, and total
length, etc. The series of software include many algorithms to provide precise detection
and measurement of the root’s features. In addition, it includes statistical methods, such
as ANOVA and 95% CI, to find significant difference among root groups with genotypes
such as natural variants and developmental mutants. The artificial neural network will
soon be added to classify root groups.
Field Based Phenotypic Platform for Characterizing Maize Growth and
Development
Sara Tirado, University of Minnesota
Advances during the past decade have allowed researchers to link genomic information
to phenotypic information and through
this enhance crop productivity. Further progress in the ability to link genotypes,
environments, and phenotypes has been limited by the accuracy and consistency of
measuring traits of agronomic importance on a large field-based scale. Current field-
based phenotyping efforts are time and labor intensive and therefore hinder the
development of large-scale trait datasets at multiple timepoints throughout a plant’s life
cycle. This brings a need for developing fully automated, inexpensive procedures for
objectively measuring plant traits in field settings. We have developed a procedure for
utilizing RGB drone imagery to extract phenotypic traits of importance, including stand
count, plant height, canopy closure, and growth rate. This platform was used to
characterize a maize association mapping population that consists of 500 diverse inbred
lines grown in replicate in Saint Paul, MN in 2017. In total five flights were conducted
throughout the growing season. Results of repeatability across replicates and variation in
growth rates as measured through plant height and canopy closure will be presented. The
application of this technology can be used to deepen our understanding of how genetic
variation and environmental influences shape the traits of corn plants in the field.
The genetic and mechanistic bases of photosynthetic cold tolerance in
legume, cowpea (vigna unguiculata (l.) walp.) via high throughput
environmental phenotyping
Donghee Hoh, MSU-DOE Plant Research Laboratory
Increasing crop production will require improvements in the efficiency, robustness, and
sustainability of photosynthesis. Among the most critical abiotic stresses that impact
photosynthesis is temperature. Cowpea (Vigna unguiculata (L.) Walp.), an important
protein source worldwide especially in developing countries was chosen as a model crop,
is especially sensitive to high temperature during reproductive development result in
severe crop losses by causing male sterility and fruit abortion. One proposed approach to
minimizing the impact of heat stress is to plant earlier than the normal to avoid extreme
heat stress later in the season. This strategy involves planting at colder temperatures,
which is generally known to retard germination and emergence in Cowpea. A major
concern is how the chilling stress impacts plant performance/yield. The goal of this
research is to identify genes and mechanisms related to chilling stress tolerance. Cowpea
has potentially sufficient genetic variation that we can use to identify quantitative trait
loci (QTL) that can be used to guide plant breeding, and test mechanistic models to
explain this sensitivity. Two sets of recombinant inbred lines (RILs) were phenotyped
using the Dynamic Environment Phenotype Imager (DEPI) and MultispeQ under control,
cold temperature conditions, under lighting conditions that simulate typical daylight. We
found considerable natural variation in the photosynthetic parameters which were used
to identify QTLs specific to cold tolerance that can be exploited for quantitative trait locus
(QTL) mapping and subsequent breeding efforts. Phenotyping and QTL data showed cold
sensitivity is strongly associated with increased photoprotective responses (qE and qI)
that is strongly linked to variations in the chloroplast ATP synthase activity, leading us to
propose a model for low-temperature photodamage that involves control of proton motive
force-induced damage to photosystem II. We also identified potential genetic control
element that could explain the adaptation of certain variants to low temperature.
Deciphering the association of phenome and gene expression postulating
salt tolerance mechanism in a rice landrace, Horkuch
Sabrina Elias, , University of Dhaka and University of Nebraska Lincoln
Rice production in the salty cultivated soil cannot meet the demand of the overgrowing
population as rice plant and excess salt has a rival relationship. Changes in climate is
worsening the scenario by introgression of excess salt in more and more cultivable lands.
But rice, as a major staple food need to maintain the balance in the production requiring
the need of high yielding salt-tolerant rice. Understanding the mechanism of salt tolerant
landraces with adaptive capability to withstand the harsh environment can give insights
on potential candidate genes for conferring tolerance. In order to do so, the reciprocal
cross of a salt tolerant landrace Horkuch and high yielding but sensitive variety, IR29 has
been analyzed. A set of the reciprocal F2:3 population was genotyped using DArTSeq™
for discovering SNP markers to construct linkage map and manual phenotyped under salt
stress. We have identified expression QTLs (eQTL) combining the genotyping and
RNAseq data of a subset under 150mM salt stress. An image-based non-destructive
automated and continuous phenotyping over 3 weeks of salt stress was carried out on a
selected F3 and F5 sub-populations followed by QTL identification for the digital traits
and relative growth rates from visual image data. Instead of endpoint records, image
analysis over days gave us longitudinal data, which could separate the early and late
responses to salt stress. Combining the phenomics and eQTL data, early growth indices
were found to be enriched with transport, osmotic response etc and the later stages were
enriched with genes associated with growth, carbohydrate metabolism, organ
development etc. The phenome data along with the expression data could give a
comprehensive scenario regarding potential candidates involved in tolerance mechanism.
Computational Classification of Phenologs across Biological Diversity
Ian Braun, Iowa State University
Phenotypic diversity analyses are the basis for research discoveries that span the
spectrum from basic biology (e.g., gene function and pathway membership) to applied
research (e.g., plant breeding). Phenotypic analyses often benefit from the availability of
large quantities of high-quality data in a standardized format. Image and spectral
analyses have been shown to enable high-throughput, computational classification of a
variety of traits across a wide range of phenotypes. However, equivalent phenotypes
expressed across individuals or groups that are not anatomically similar can pose a
problem for such classification methods. In these cases, high-throughput, computational
classification is still possible if the traits and phenotypes are documented using
standardized, language-based descriptions. In the case of text phenotype data, conversion
to computer-readable “EQ” statements enables such large-scale analyses. EQ statements
are composed of entities (e.g., leaf) and qualities (e.g., length) drawn from terms in
ontologies. In this work, we present a method for automatically converting free-text
descriptions of plant phenotypes to EQ statements using a machine learning approach.
In each description, words related to entities and qualities are identified using the
CharaParser annotation tool (Cui, 2012). A classifier identifies potential matches between
these words and terms from a set of ontologies, including GO (gene ontology), PO (plant
ontology), and PATO (phenotype and trait ontology), among others. The features used by
this classifier include semantic, syntactic, and context similarity metrics between words
and ontology terms. This classifier is trained and tested using a dataset of manually
converted plant descriptions and EQ statements from the Plant PhenomeNET project
(Oellrich, Walls et al., 2015). The most likely matching terms identified by the classifier
are used to compose EQ statements. Any obtained results of these automated conversions
in terms of precision and recall will be presented. Potential use across datasets to enable
automated phenolog discovery are discussed.
Machine vision phenotyping platform for seedling growth and morphology
Cory Hirsch, PhD, University of Minnesota
The ability to link genotypes and phenotypes can be used to improve plant productivity
and our understanding of plant biology. Our ability to obtain genomic information
efficiently and accurately has advanced greatly, while phenotyping methods have largely
remained laborious, subjective, and/or expensive. Towards alleviating these barriers, we
have developed a user friendly and affordable platform to acquire highly standardized
RGB images, while relying on minimal equipment and space in a laboratory setting.
Currently, we have developed algorithms to extract numerous growth and morphological
traits including plant height, width, stem diameter, pixel area, and center of mass. The
traits our algorithm extract correlate well with both traditional hand measurements and
measurements using manual image analysis techniques. We are leveraging available
storage, application deployment, and compute resources through the infrastructure at
CyVerse to allow accessibility to almost any researcher. This platform is already being
used by multiple research groups at multiple Institutions and has been optimized for daily
collection of images of multiple plants to easily look at plant development. We have used
this method to monitor growth rate variation among different maize genotypes subjected
to temperature stress and also to measure variation in heterosis for seedling growth in a
panel of inbred and hybrid genotypes.
Machine learning approaches in Soybean Phenomics: Predicting Seed
Yield, Oil and Protein in Contrasting Production Systems
Kyle Parmley, Iowa State University
Genetic improvement of soybean [Glycine max (L.) Merr.] has permitted the expansion
of soybean across a broad geographic region. Past breeding efforts have attempted to
develop highly stable cultivars to deploy across all production systems, but these
genotypes may evade an advantageous genotype by management (G x M) interaction, i.e.,
row width spacing. The development of these production system targeted cultivars will
require continual improvement of yield per acre of soybean, which in turn will be
dependent on the modification of physiological traits. Advances in remote sensing
technologies have enabled rapid measurements of these traits on a temporal and spatial
scale, and therefore are becoming increasingly adopted in advanced breeding systems.
The objective of this study to develop yield prediction models using machine learning
approaches. We used two independent studies with 32 genotypes of the SoyNAM panel
with contrasting treatments: row width spacing (38 and 76 cm) and seeding density (123,
345, 568 x 103 seeds ha-1) from nine environments in replicated tests. Physiological trait
data of hyperspectral reflectance, leaf area index, canopy temperature, light interception,
and chlorophyll content were collected at three time points during the growing season.
Robust in-season prediction models identified informative explanatory varaiables for
seed yield, oil and protein predictions, which and will aide in breeding applications for
contrasting production systems. Preliminary results indicate prediction accuracies were
also similar for remote sensing tools with moderate and high throughput capability
thereby decreasing the temporal requirement for data acquisition. The application of
these approaches enable a mechanistic understanding of yield drivers in contrasting
production systems and enable more informative decision making capability.
Advances in sensing for high-throughput in-field and postharvest crop
phenotyping
Sindhuja Sankaran, PhD, Washington State University
Washington State University
Phenomic advancements to evaluate crop interaction with environment is a rapidly
developing area of research. In last 5 years, applications of sensing technologies in
breeding programs have increased dramatically, where multiple sensors at different
scales have been utilized to assess different traits from yield potential, biotic and abiotic
stress tolerance in field conditions to postharvest crop traits such as seed quality.
Washington State University and USDA-ARS units in the state of Washington have
several active breeding programs from cereal grains to specialty crops. Some of these
program is using sensing tools for high-throughput and accurate phenotyping as a part of
their ongoing crop improvement efforts. This talk will discuss some of these
developments in phenomic research, data analytics associated with sensing, and
applications for infield crop and postharvest crop trait evaluation.
A Bayesian approach to quantitative genetics for high-dimensional traits
Daniel Runcie, PhD, University of California Davis
Statistical models for Genome-Wide Association Studies, QTL analysis, and Genomic
Prediction, are the foundation of modern quantitative genetics and crop improvement.
Driven by the explosion of whole-genome genotype data, recent improvements to these
models allow for analyses of millions of markers at a time. However, similar advances for
modeling large phenotype datasets is lacking. New phenotyping technologies collect
thousands of observations on each individual plant or line – changes in morphology
through time, molecular phenotypes such as gene expression or metabolite levels, or
performance measures across multiple environments. Jointly modeling these high-
dimensional traits can provide insight into developmental and physiological mechanisms
that link genotype and phenotype. We propose a robust and efficient method for modeling
the genotype-phenotype relationship of high-dimensional traits. The key idea underlying
our model is that groups of traits will be highly correlated due to genetic and
developmental pleiotropy. We leverage these correlated modules to prioritize the most
important signals in big data. We will demonstrate how our method provides powerful
and interpretable estimates of genetic architecture using two high-dimensional datasets:
a time-series analysis of growth curves, and a dataset of genome-wide gene expression.
Network design principles of plant shoot architectures
Saket Navlakha, PhD, The Salk Institute for Biological Studies
Saket Navlakha; Arjun Chandrasekhar; Adam Conn; Ullas Pedmale; and Joanne Chory
Salk Institute for Biological Studies
Transport networks serve critical functions in biological and engineered systems, and
their design requires trade¬offs between competing objectives. Plants need to optimize
their architecture to efficiently acquire and distribute resources while also minimizing
costs in building infrastructure. To understand how plants resolve this design trade¬-off,
we used 3D laser scanning to map hundreds of shoot architectures of tomato, tobacco,
and sorghum plants grown in several environmental conditions and through multiple
developmental time-points. Using a graph¬-theoretic algorithm that we developed to
evaluate design strategies, we find that plant architectures lie along the Pareto front
between two simple objectives — minimizing total branch length and minimizing nutrient
transport distance — thereby conferring a selective fitness advantage for plant transport
processes. The location along the Pareto front can distinguish among species and
conditions, suggesting that during evolution, natural selection may employ common
network design principles despite different optimization trade-¬offs. We also find similar
trade¬-offs sculpting the shape of neural dendritic and axonal arbors, suggesting shared
properties of branching processes in two very different biological systems.
Using mathematics to dissect and quantify the plant form, above and
belowground
Mao Li, PhD, Donald Danforth Plant Science Center
Donald Danforth Plant Science Center
The genome encodes the entire growing plant form, both above and below ground. Yet
conventional phenotypic measures usually consider only isolated parts of the plant to
understand genotype-to-phenotype relationships. Manually dissecting each individual
part is either labor intensive or missing integrative information, limiting our
understanding of genetic and environmental conditioning of the plant form. Using
mathematical approaches such as persistent homology based methods, combined with
imaging technologies and statistical tools could automatically dissect and
comprehensively quantify plant morphology at different scales. We describe some
examples of application such as sorghum panicle, grape rachis, root, and leaf.
Concurrent III: Integrating phenotypes through modeling Friday, February 16, 2018 | 2:30 PM – 5:30 PM
Coupling terrestrial LiDAR measurements of tree architecture with high-
resolution biophysical models to provide insights into plant-environment
interactions
Brian Bailey, PhD, University of California, Davis
Recent advances in the development of high-resolution phenotyping methods coupled
with detailed biophysical models has opened the door to a new frontier in the study of
plant-environment interactions. Methods have been developed to digitize the
architecture of roots and shoots, which can be used as inputs for highly detailed models
that can help to connect the dots between measurements. Despite these recent advances,
many challenges remain. The focus of this work is on addressing the challenge of
phenotyping and modeling plant systems at high resolution across scales from leaves to
canopies.
New phenotyping methods are presented that use terrestrial LiDAR scanning data to
rapidly measure the leaf area and angle distributions of large plant such as trees, which
are ultimately used to perform leaf-by-leaf reconstructions that can be directly fed to leaf-
resolving models. The method uses a statistical backfilling approach that is applicable
even in dense trees where the majority of foliage or woody area is not visible to the
scanner. This detailed structural data is used to provide inputs for leaf-resolving models
of radiation, microclimate, evapotranspiration, and photosynthesis. Model complexity is
afforded by performing calculations in parallel using graphics processing units (GPUs),
which allows for simulations that can resolve an unprecedented range of scales spanning
leaves to canopies.
Linking solar induced fluorescence with photosynthetic variability in crops
at the leaf and plot scales.
Caitlin Moore, University of Illinois
Katherine Meacham – University of Illinois; Guofang Miao – University of Illinois;
Taylor Pederson – University of Illinois; Evan Dracup – University of Illinois; Xi Yang –
University of Virginia; Kaiyu Guan – University of Illinois; Carl Bernacchi – Univ
The measurement of solar induced fluorescence (SIF) of chlorophyll has emerged as a
useful tool for monitoring plant photosynthesis. Application of SIF at the regional scale
from satellite remote sensing has delivered promising results, with strong links found
between SIF and gross primary productivity. Our ability to use SIF as a tool to monitor
photosynthesis has the potential to enhance agricultural advancement by facilitating the
identification of better performing individuals at a faster rate. However, this kind of high-
throughput phenotyping is usually achieved at the plot and/or leaf scale and there
remains an understanding gap as to what extent SIF can capture plot and leaf scale
variation in photosynthetic activity. We tested the ability of SIF to capture photosynthetic
variability at the plot scale in a C4 biomass sorghum field and at the leaf scale in a C3
tobacco experiment grown under field conditions in Central Illinois, USA. To do this, we
built a portable SIF system to collect high-resolution measurements of SIF and coupled
these with measurements of leaf-level gas exchange and photosynthetic performance
indicators (i.e. Vcmax & Jmax), in situ chlorophyll fluorescence, leaf chlorophyll content,
spectral reflectance indices and leaf area. This presentation will discuss not only the
results from our field experiments, but also some of the lessons we have learned along the
way towards developing SIF into a high-throughput phenotyping tool for use at the leaf
and plot scales.
Put the carbon back into the soil: 3D root phenotyping for improved carbon
sequestration
Suxing Liu, University of Georgia
Suxing Liu – University of Georgia; Alexander Bucksch – University of Georgia
Carbon rich soil ensures the fertile soils and the agricultural productivity of plants that
sequester atmospheric carbon available as CO2 into the soil. Deeper rooting crops are the
key to increase carbon content below ground and to improve soil quality and agricultural
output. Root phenotyping is crucial since it provides avenues to quantify deeper root traits
of important crops like maize. However, due to the opaque nature of soil, dense and highly
occluded maize root system, quantifying these traits such as whorl number, distance and
number of crown roots is very challenging.
We developed a 3D reconstruction method that completely digitizes the maize root
architecture. The classic structure from motion algorithm was not able to reconstruct
detailed inner roots architecture without manual registration, we extends this method to
produce a dense point could root model based on images collections taken only from the
outside of roots. Our extensions allow automated reconstruction and measurement of the
inner occluded root system.
We demonstrate the quality of our method on two maize genotypes with six replicates for
each genotype. Our 3D root model reconstruction method is a first promising step
towards automated quantification of highly occluded maize root system. And it enable the
discovery of genes associated with deeper rooting by molecular biologists and pave a
promising way to increase soil carbon sequestration in crops.
A modular, community-driven framework for developing high-throughput
plant phenotyping tools
Noah Fahlgren, Donald Danforth Plant Science Center
Malia Gehan – Donald Danforth Plant Science Center; Arash Abbasi – Donald Danforth
Plant Science Center
Systems for collecting image data in conjunction with computer vision techniques are a
powerful tool for increasing the temporal resolution at which plant phenotypes can be
measured non-destructively. Computational tools that are flexible and extendable are
needed to address the diversity of plant phenotyping problems. To address these needs,
we developed PlantCV, an open-source framework for analyzing high-throughput plant
phenotyping data. The goal of the PlantCV project is to develop a set of modular, reusable,
and repurposable tools for plant image analysis that are open-source and community-
developed. PlantCV was originally developed to analyze data from the Bellwether
Phenotyping Facility at the Donald Danforth Plant Science Center, but the set of available
features has grown as the set of users and use cases have diversified. Here we focus on
several recent developments within the PlantCV project, including 1) Robust and
automated methods to test and review code and documentation through the GitHub
platform to manage diverse community activity; 2) Utilization of containerization
technologies and integration with the Open Science Grid and Amazon Web Services
computing platforms to improve the scalability and deployability of PlantCV; and 3)
Machine learning applications for plant feature analysis. We are using these tools, in
conjunction with high-throughput imaging platforms, as a method to dissect the genetics
of plant traits for bioenergy crops such as sorghum and camelina. PlantCV is open source
and is available at http://plantcv.danforthcenter.org.
Non-linear plant phenotyping pipelines: how can structural models and
machine learning can help us analyse large plant image datasets
Guillaume Lobet, PhD, Forschungszentrum Juelich
Institute of Bio- and Geosciences Agrosphere (IBG-3)
Many structural root models have been developed, either generic or for specific species,
and these have repeatedly been shown to faithfully represent the root system structure,
as well as being able to output ground-truthed data for every simulation and image,
independent of root system size. Here we will show that structural root models can be
used in combination with image analysis pipelines to assess and improve their overall
performance. First, we will show that an in-depth analysis of root image analysis pipelines
using such models reveals strong limitations in their ability to measure complex root
systems. Secondly, we will present an innovative strategy that combines root models and
machine-learning algorithms (random-forests), that can increase the measurement
accuracy.
Gravimetric phenotyping of canopy conductance in wheat and maize
reveals novel mechanisms, traits and genetic loci involved in drought
tolerance in the field
Walid Sadok, University of Minnesota
Walid Sadok – University of Minnesota; Bishal Tamang – University of Minnesota;
Remy Schoppach – University of Minnesota
Canopy conductance plays a critical role in crop drought tolerance. Particularly in
Mediterranean-type environments where crops grow on stored soil moisture, this process
controls how much water is being traded for CO2 and the soil moisture budget available
for the crop. As a result, phenotyping canopy conductance is currently a major target of
breeding programs worldwide, but is still notoriously problematic in the field. To
circumvent those limitations, we developed a gravimetric approach to phenotyping
whole-plant canopy conductance under controlled conditions where conductance is
measured as the slope of whole-plant transpiration rate (measured via balances
connected to loggers) in response to increasing vapor pressure deficit. Using this
approach, we phenotyped a wheat mapping population revealing QTL for canopy
conductance most of which co-localized with QTL of grain yield measured in 11 locations
in Australia and Mexico. Further, we identified a major QTL that was mapped to genes
suggesting the involvement of root hydraulics in controlling canopy conductance, which
was confirmed in independent experiments. We also discovered substantial genetic
variability in nocturnal transpiration in this population that was controlled by several
QTL, most of which co-localized with yield QTL, a relationship that we confirmed via
simulation modelling. In maize, we recently deployed this approach on 25 parents of the
maize Nested Association Mapping panel (McMullen et al. 2009). Our investigation
revealed substantial variability in canopy conductance and nocturnal transpiration
among those lines. We also discovered a circadian control of nocturnal transpiration that
was genotype-dependent, which seemingly modulates the level of canopy conductance
during the day. Those findings illustrate ways gravimetric phenotyping of water use in
crops can lead identifying novel traits that can inform breeding programs while
illuminating novel biological insights.
References:
Tamang and Sadok (2018). Env. Exp. Bot. In press
Schoppach et al (2016). J. Exp. Bot. 67, 2847-2860
Use of biophysical first principles to select plant traits and the instruments
and analyses to measure and explain them
Brent Ewers, University of Wyoming
Predictive understanding of genotypic variation in growth and response to drought
requires quantifying a constellation of plant traits such as leaf and root physiology and
allocation to both of those organs and reproduction. High-throughput phenotyping of
these traits that connect to emerging –omics approaches are limited by instrumentation
and data analyses. Moreover, engineering new instruments is often hampered by lack of
clarity in what traits to measure and how to analyze the data. We suggest a new approach
in which both high and low throughput traits that best lead to improved predictions are
hypothesized from biophysical model outputs. Our biophysical, first principles model
suggests that traits related to quantum yield of photosynthesis, water transport
limitations from roots to leaves, and allocation to roots, leaves and reproduction are most
important in predicting growth from the crop plant Brassica rapa. We test these
instruments and data analyses across a diverse panel of crop accessions and response to
drought. We found that chlorophyll fluorescence measurements quantified quantum
yield in both high and low throughput instruments with direct comparisons not
statistically different from unity. We assessed root hydraulics and root area through low
throughput means and found proxies derived from electrical conductivity, biomass
allocation, and image analysis allowed the model to predict growth. Other tests include a
temporally dense experiment of Brassica rapa response to drought that was analyzed with
a Bayesian network model that combined gene expression modules from RNA-Seq with
our measured traits. The resulting probability tables illustrated that the gene models
provided more predictive power over the temporal changes in the traits than the network
of the biophysical traits themselves. Moreover, the gene ontologies from these modules
describe hypotheses for further experiments such as testing how night transpiration
decline in drought is related to up regulation of nitrate transporters.
Genome-to-Phenome Mapping by Metabotyping in Brachypodium
distachyon: Exploring Genotypic Diversity for Biomass Accumulation and
Shoot-Root Allometry
Christer Jansson, Pacific Northwest National Laboratory
The present study aims to explore genotypic diversity for biomass accumulation and
shoot-root allometry in natural accessions of the annual C3 grass Brachypodium
distachyon (Brachypodium) under two contrasting watering regimes, well-watered and
episodic drought (henceforth referred to as control and drought conditions, resepctively),
and to what extent genotypic and phenotypic diversity correlate with metabolite profiles.
External phenotypes like biomass accumulation and shoot-root allometry represent
emergent properties, and as such are informed by internal phenotypes, e.g., biochemical
and physiological properties, in interaction with the environment. We argue that bridging
the gap between genotype and external phenotype can be facilitated by a two-step process,
whereby linkages are established between genotype and internal phenotype and between
internal and external phenotypes. In considering internal phenotypes, we point to
metabolomics as an emerging tool to provide insight into how genotypic diversity affects
phenotypic variation in plants, although it should be noted, that the genetic control of
plant metabolomes remains all but unknown, and that even in a system such as E. coli
interactions between gene variants and metabolite profiles are poorly understood. The
method for genome-to-phenome mapping, envisioned here with a moderate-sized set of
genotypes (30 accessions) and powerful statistical processing, is to proceed in two steps
by establishing a composite internal phenotype, e.g., with genotype-associated
metabotypes containing clusters of detected metabolite features) and biomass-associated
metabotypes containing profiles of detected metabolite features and their relative
abundance. The internal phenotype will then serve as a proxy for the external phenotype.
Our results suggest that this phased strategy holds great promise for further exploration
with increased number of accessions and biological replicates and with continued
development of computational signal discovery algorithms to allow for integration of
independently generated sets of metabotypes.
Concurrent IV: Crop Biology Friday, February 16, 2018 | 2:30 PM – 5:30 PM
Insights into the genotype-by-environment interaction enabled through
phenomics
Candice Hirsch, PhD, University of Minnesota
Candice N. Hirsch1, Zhi Li1, Sara B. Tirado1,2, Michael R. White3, Celeste Falcon3,
Nathan D. Miller4, Edgar P. Spalding4, Natalia de Leon3, Shawn M. Kaeppler3, Patrick
S. Schnable5, Nathan M. Springer2
The ability to measure variation in plant genomes is becoming routine due to advances in
sequencing technology and analysis methodologies. Our ability to understand the
interaction of genomic variation with the environment is still largely limited by the
phenotypic traits that can be measured with regards to number of traits, accuracy of trait
measurements, and the frequency at which traits can be measured. As with sequencing
technologies, we are seeing rapid advancements in sensor technologies, platforms to
deploy those sensors, and analysis pipelines that are now allowing for plants to be
measured in a range of environmental conditions to allow for new insights to be made in
the interaction of genotype and environment. We have deployed a previously developed
pipeline that enabled the evaluation of a large number of traits on ears, cobs, and kernels
to further our understanding of the genotype-by-environment interaction. This pipeline
has been used to study the impact of environmental variation on yield potential and yield
component traits, the impact of environment on observed heterosis, and the impact of
environment on small introgressions in the genome (i.e. those that would be present
during the introgression of a large effect QTL or backcrossing in a transgene). Further
advances in our ability to measure plants in real time across diverse environments will
continue to advance biological insights that can be made into the interaction of the
genome and environment.
Genetic control of soybean (Glycine max L. Merr.) shoot architecture
Kamaldeep Virdi, PhD, University of Minnesota
Gary Muehlbauer – University of Minnesota; Robert Stupar – University of Minnesota;
Aaron Lorenz – University of Minnesota; Austin Dobbels – University of Minnesota;
Suma Sreekanta – University of Minnesota; Jeffrey Roessler – University of Minnesota
Soybean shoot architecture is a complex phenotype, which can be partitioned into various
measurable traits. Variation in shoot architecture likely influences canopy light
interception, photosynthesis, and source-sink partitioning efficiency, and thus is related
to overall grain yield. Here, shoot architecture-related traits are defined as branch angle,
branch number, branch length, leaf shape and size, petiole length, petiolule length,
canopy coverage, days to flowering, maturity, determinancy, and light penetration
through the canopy. A detailed phenotypic characterization of these shoot architecture-
related traits, their contributions to overall shoot architecture, and their genetic control
is imperative to fully exploit the yield potential of soybean. We examined a set of 400
diverse maturity group 1 soybean accessions to study the natural variation for shoot
architecture-related traits, to identify relationships between traits, and to use association
mapping to identify loci that are associated with shoot architecture traits. The panel was
genotyped with 32,650 SNP markers. To collect phenotype data for shoot architecture-
related traits, we used a combination of high-throughput (drone and ground-based
imagery) and conventional phenotyping platforms. Significant QTL associated with
branch angle, leaf length/width ratio, petiolule length, days to flowering, maturity and
stem termination were detected. In most cases, these QTL overlapped with previously
detected genes or QTL. For example, a leaf length/width ratio QTL on chromosome 20
was found to be coincident with the location of a previously isolated Narrow Leaf (Ln)
gene. Interestingly, we detected a branch angle QTL located on chromosome 19 that
overlaps with QTL associated with canopy coverage and light penetration, suggesting
branch angle is an important determinant of canopy coverage.
Field Phenotyping of Grain Crops Response to Agronomic Inputs
Steven Shirtliffe, PhD, Department of Plant Sciences, University of Saskatchewan
Hema Duddu – Department of Plant Science, University of Saskatchewan; Menglu
Wang – Department of Plant Science, University of Saskatchewan; Seungbum Ryu –
Department of Plant Science, University of Saskatchewan; Scott Noble – Department of
Mechanical Eng
High throughput phenotyping of grain crops has become a dynamic research areas with
applications in plant breeding and genomics. Field based phenomics quantifies remotely
quantifies visual phenotypes to associate with regions of the genome. These techniques
also have the potential to revolutionize agronomic research. We will present several
examples of current research utilizing remotely gathered imagery including, response to
nitrogen fertilizer, simulated hail damage, herbicide tolerance and plant spatial
arrangement. Phenotypes were extracted from this imagery with a variety of techniques
ranging from vegetation index analysis, classic image analysis, and deep learning. UAV
based field imagery offers a wealth of data to “ground truth” phenotypes for use in remote
crop diagnostics and precision agriculture. Furthermore this research is directly
applicable classic phenomic research as the response to crop inputs is the manageable
part of genotype by environment interaction. Finally, as the range of phenotypes
expressed to agronomic inputs is usually greater than that within elite germplasm, more
precise calibration of phenotypes is possible. Synergies exist between phenotyping genetic
and agronomic effects in crops < ./p>
Forward Phenomics of oat Panicles
Abdullah A Jaradat, USDA/ARS & University of Minnesota
There is a growing need for adapted and more productive germplasm to expand oat
production, optimize its yield, improve groat quality, and satisfy farmers and consumers
demand, especially in the Upper Midwest of the US. Oat germplasm, representing
different eco-geographical origins and breeding status, was characterized and evaluated
using field- and laboratory-based forward phenomics. Whole plots were phenotyped at
successive growth stages during three growing seasons using aerial and hand-held
imagery and sensors. Digital and high-throughput data were captured and compiled on
(1) color space descriptors of whole plots during key growth stages, (2) growing degree
days to panicle emergence and maturity; (3) phenotypic and structural traits of panicles
and spikelets; and (4) groat quality. A relational database was mined and statistically
analyzed to (1) cluster the oat germplasm using unsupervised hierarchical clustering
method; (2) identify traits with positive or negative, direct or indirect effect on the panicle
phenome; (3) identify a minimum set of traits which can discriminate between
structurally and agronomically different panicle phenotypes; (4) express groat weight per
plant as a function of stochastic panicle architecture and (5) quantify the effect of panicle
architecture traits that have implications for groat quality. A dynamic custom profiling
procedure was instrumental in quantitatively assessing the importance of, and visually
adjusting structural panicle traits to predict and optimize groat agronomic and quality
traits.
Data fusion with light detection and ranging and images to map and count
bolls in upland cotton
Alison Thompson, PhD, USDA-ARS
Andrew French – USDA-ARS; Michael Gore – Cornell University; Alex Conrad – USDA-
ARS
Terrestrial Light Detection and Ranging (LIDAR) has the potential to provide accurate
boll counts and maps of boll distribution over a defoliated cotton canopy. Using a field-
portable LIDAR unit and GPS receiver, cotton plants can be scanned plot-by-plot,
returning point cloud data that can be aggregated and mapped. Since 2013 small plot
LIDAR experiments have been conducted at Maricopa, AZ to develop acquisition and data
processing systems. Results, achieved using multiple mobile platforms including high
clearance tractors, proximal sensing carts, and semi-robotic rail carts, have shown that
cotton bolls can be mapped and counted provided, platform travel speeds are slow (~0.5
m/s), GPS locations are accurately synchronized with the LIDAR, and cotton plants are
fully defoliated. However, obtaining consistent results has been difficult due to slow
LIDAR scan rates, leading to spatial sampling gaps and inaccurate sensor positioning.
Experiments in 2017 were undertaken to reduce these problems by adopting a data fusion
approach. We deployed a LIDAR unit on a motorized proximal sensing cart capable of
scanning at rates exceeding 200 Hz, in parallel with 3 RGB cameras with multiple view
angles to generate high resolution point cloud data sets. The data sets were
complementary, where the LIDAR provided mm-level accuracies and the camera data
provided spatially contiguous observations. Data collected in November 2017 over ~0.5
ha-1 of variable density upland cotton and corresponding validation measurements of boll
counts and boll distribution will be presented.
UAS Phenotyping in Soybean Breeding and Phenomic Inference
Katy Rainey, Purdue University
Katy Rainey, PhD – Purdue University
Abstract: Phenomics and data-driven selection in crop breeding encompass applying new
and differently-used data to increase selection efficiency of crop varieties. To begin to
understand the potential applications of novel phenotypes, an initial stage of phenomic
inference is helpful. Defining features of the phenomic inference stage are high
phenotypic variance for yield potential and control of the confounding effects of crop
development. For “new” traits, this stage provides a first report of genetic architecture,
quantitative properties such a genetic correlation to yield, and initial remote sensing
prediction equations. Mixed model statistical procedures are the foundation for achieving
these aims, and for characterizing novel phenotypes. Analytical innovations are needed
in several areas, including predictive analytics and new approaches to genetic inference
of development. We present case studies from a soybean breeding pipeline where we use
UAS-acquired RGB imagery to select multiple agronomic traits. We are also developing
analytical tools from operations, to analysis, to decision making for UAS RGB and
multispectral imagery.
Whole-plant stress performance analysis: A new tool for functional
phenotyping
Menachem Moshelion, The Hebrew University of Jerusalem
Abiotic stress factors limit crop yields and long-term food security. It is widely accepted
that phenotyping tools are needed to bridge the gap between the plant molecular profile
and plant performance, particularly under stress conditions.
The need for physiological phenotyping of the whole plant led us to develop a new and
effective functional-phenotyping platform, as well as real-time analytical software to
efficiently extract knowledge from the collected data. This platform enables the
simultaneous and continuous monitoring of water relations in the soil–plant–
atmosphere continuum of numerous plants under dynamic environmental conditions.
This system provides a simultaneously measured, detailed physiological response profile
for each plant in the array over time periods ranging from a few minutes to the entire
growing season, under normal, stress and recovery conditions and at any phenological
stage. Three probes for each pot in the array and a specially designed algorithm enable
the detailed characterization of whole-plant transpiration, biomass gain, stomatal
conductance, whole-plant water-use efficiency and relative water content under dynamic
soil and atmospheric conditions. The system has no moving parts and can be used in many
experimental settings.
Our algorithms are based on multi-factorial analysis of variance and the scoring of all
physiological traits. This comparative, performance-analysis approach enables the rapid
selection of plants with the desired physiological behaviors, which is very important in
pre-breeding for stress tolerance.
The screening of many plant species, using our system and conventional gas-exchange
tools, has confirmed the accuracy of the system and its diagnostic capabilities. In addition,
the quantitative physiological traits measured have been shown to be closely correlated
with total dry biomass. In the future, this platform could help with early yield prediction
and enhance functional-breeding programs.
General Session III Saturday, February 17, 2018 | 8:30 AM – 12:30 PM
Multiscale modelling of plant-soil interaction
Tiina Roose, MSc, DPhil (PhD), University of Southampton
University of South Hampton
In this talk I will describe a state of the art image based model of the soil-root interactions,
i.e., a quantitative, model of the rhizosphere based on fundamental scientific laws. This
will be realised by a combination of innovative, data rich fusion of structural imaging
methods, integration of experimental efforts to both support and challenge modelling
capabilities at the scale of underpinning bio-physical processes, and application of
mathematically sound homogenisation/scale-up techniques to translate knowledge from
rhizosphere to field scale. The specific science question I will address with these
techniques is how to translate knowledge from the single root scale to root system, field
and ecosystem scale in order to predict how the climate change, different soil
management strategies and plant breeding will influence the soil fertility.
The Plant Phenology Ontology: A new informatics resource for large-scale
integration of plant phenology data
Robert Guralnick, University of Florida
University of Florida
Plant phenology — the timing of plant life-cycle events, such as flowering or leafing out —
plays a fundamental role in the functioning of terrestrial ecosystems, including human
agricultural systems. Because plant phenology is often linked with climatic variables,
there is widespread interest in developing a deeper understanding of global plant
phenology patterns and trends. Although phenology data from around the world are
currently available, truly global analyses of plant phenology have so far been difficult
because the organizations producing large-scale phenology data are using non-
standardized terminologies and metrics during data collection and data processing. To
address this problem, we have developed the Plant Phenology Ontology (PPO). The PPO
provides the standardized vocabulary and semantic framework that is needed for large-
scale integration of heterogeneous plant phenology data. The PPO was designed to be
applicable to (nearly) all gymnosperms and angiosperms, suitable for both single plants
and populations of plants, compatible with the data and data collection methods of
existing national or regional monitoring program in the USA and Europe, and
interoperable with existing OBO Foundry library ontologies, especially the Plant Ontology
and the Biological Collections Ontology. Here, we more fully describe the PPO, and we
also report preliminary results of using the PPO and a new data processing pipeline to
build a large dataset of phenology information from North America and Europe. We close
by discussing future Plant Phenology Ontology efforts, along with the social and technical
tools for further developing global in-situ phenology products and analyses, including
phenology sensor data.
Division plane orientation in plant cells
Carolyn Rasmussen, PhD, University of California, Riverside
One key aspect of cell division in multicellular organisms is the orientation of the division
plane. Proper division plane establishment significantly contributes to normal
organization of the plant body. To determine the importance of cell geometry in division
plane orientation, we designed a three-dimensional probabilistic mathematical modeling
approach based on century-old observations: equal daughter cell volume and the
resulting division plane is a local surface area minimum. Predicted division planes were
compared to a plant structure that marks the division site, the preprophase band (PPB).
PPB location typically matched one of the predicted divisions. Predicted divisions offset
from the PPB occurred when a neighboring cell wall or PPB was observed directly
adjacent to the predicted division site, as to avoid creating a “four-way junction”.
Population level modeling accurately predicted ~95% of in vivo cell divisions based on
geometry. This powerful model can be used to separate the contribution of geometry from
mechanical stress or developmental regulation in predicting plant division plane
orientation.
Automated plant architectural trait extraction from a field-based high-
throughput phenotyping platform
Maria Salas-Fernandez, PhD, Iowa State University
Iowa State University
Yield is determined by the plant’s capacity to capture light energy and utilize it to fix CO2
into complex organic compounds. This capacity is mostly determined by both the
biochemical process of carbon fixation and the arrangement of leaves throughout the
plant. The large scale investigation of leaf angle variation throughout the canopy and
other plant architecture traits that determine biomass yield is labor intensive, time
consuming, expensive and, in some cases, impossible to accomplish using manual
measurements. We have examined the accuracy of machine vision to estimate plant
architectural traits of a large set of sorghum lines under field conditions. Our approach is
based on the 3D reconstruction of stereo images collected sideways and the automatic
extraction of novel descriptors with validated biological significance. These features
included proxies of leaf angle, leaf length and biomass volume. A comparison of image-
derived descriptors and manually collected plant architecture parameters were
performed in a subset of lines. Subsequently, novel image-derived descriptors were
utilized as phenotypic measurements in a genome-wide association analysis to discover
genes/genomic regions controlling natural variation of plant architecture traits in
sorghum. These discoveries were compared to previously reported attempts to genetically
dissect plant architecture using manually collected data and provide new knowledge
about the genetic mechanisms underlying these important traits. The completely
automated processing methods developed in our study represent a new tool for plant
breeders and advance the interdisciplinary field of high-throughput phenotyping.
Machine learning in plant phenotyping: will it relieve the bottleneck?
Sotirios Tsaftaris, MSC, PhD, University of Edinburgh
University of Edinburgh
Advances in automation and imaging equipment have created a renaissance in the
measurement of phenotyping traits in plants. While previously the bottleneck was the
image acquisition currently it is extracting those measurements from imaging data in a
reliable and automated fashion (to deal with high-throughput and high data
dimensionality) that is now the new bottleneck. This bottleneck is expected to increase
as we venture in the field (higher variability) even further. I will describe how the use of
machine learning can open the road towards relieving the bottleneck. I will present
solutions, stemming from our work and from others on automated leaf counting, plant
growth assessment, and trait identification. However, machine learning algorithms and
particularly new deep learning methods, need data to learn from. Thus, I will discuss the
need for curated annotated data, and guidelines and opportunities in collecting such data.
I will conclusively demonstrate with examples how open data can help address the
bottleneck.
The shape of plants to come: in situ computation and field math
Alexander Bucksch, PhD, University of Georgia
The genome encodes the entire growing plant form, both above and below ground. Yet
conventional phenotypic measures usually consider only isolated parts of the plant to
understand genotype-to-phenotype relationships. Manually dissecting each individual
part is either labor intensive or missing integrative information, limiting our
understanding of genetic and environmental conditioning of the plant form. Using
mathematical approaches such as persistent homology based methods, combined with
imaging technologies and statistical tools could automatically dissect and
comprehensively quantify plant morphology at different scales. We describe some
examples of application such as sorghum panicle, grape rachis, root, and leaf.
Measurements that matter: Ensuring quality and traceability of data for
agricultural insights
Michael Malone, PhD, Climate Corporation
Daniel Hangartner – Climate Corporation; Nicholas Dinchev-Vogel – Climate
Corporation
Farmers have a growing number of tools at their disposal to monitor their fields and
adjust their management practices accordingly. Unfortunately more data does not always
lead to better decisions: The old computing adage "garbage in, garbage out" can be equally
applied to digital and precision agriculture. Here we will discuss common challenges that
can undermine the utility of sensor data and propose a framework of best practices to
ensure that every new sensor and cloud data source provides value to customers and
companies alike.
Root phenotyping using X-ray technology: Automation of data
segmentation for 4D analysis
Stefan Gerth, Fraunhofer EZRT
Joelle Claußen – Fraunhofer EZRT; Norbert Wörlein – Fraunhofer EZRT; Anja Eggert –
Fraunhofer EZRT; Norman Uhlmann – Fraunhofer EZRT
During the last years, X-ray technology has been applied for the non-destructive
visualization of optical inaccessible structures in plants. Formerly, this technology was
only used for medical imaging. Nowadays, it is used as a standard tool in industrial
applications for material analysis. With X-ray computed tomography (CT) the 3D volume
information of objects can be reconstructed using X-ray projections of the object from
different points of view. A conical X-ray beam projects the plant on a 2D flat panel
detector. The resulting spatial sampling frequency is determined by the geometrical
magnification and the pixel size of the flat panel detector.
Due to the non-destructive nature of CT it is possible to track the growth of plant organs
such as cassava tubers or root systems without excavating the plants. Thus, the
belowground development of an individual plant can be observed time dependently in
natural soil. To extract the root system out of the soil, new algorithms are used for the
automatic segmentation. This allows searching for new traits with an increased
throughput compared to manual volume segmentation.
We observed the root growth of different Cassava varieties over a period of two months.
Within this timeframe, we periodically analyzed the root system and applied an
automated segmentation algorithm. Thus, we are able to characterize the volumetric
growth of storage roots time resolved and track them already at an early stage in a reliable
and reproducible manner. For example, this approach allows calculating the total volume
as a function of the depths for each of the different varieties. Due to the time resolved
experiment, this data can be used to calculate the biomass growth rates, as well. Using
this approach, we are able to segment automatically the root system in a reliable and
comparable way, even with variations in the soil humidity.
From text blobs to computable data: challenges in mining phenotypical data
from text
Hong Cui, PhD, University of Arizona
University of Arizona
In biomedical domains, ontologies have been seen as an effect approach to data
integration that brings collections of less structured data to computation. Phenotypic
character data occupies a prominent position in both evolution research aiming at
building the Tree of Life and other branches of biology interested in linking genotypes to
phenotypes to answer questions such as which genes cause what diseases. However, the
vast majority of phenotypic character information is published in (semi-)natural
languages and not directly suitable for computation. Converting such information into
ontologized statements such as Entity Quality (EQ) has been underway, along with initial
signs of success in using ontologized phenotypic characters. The mining and the curation
processes, however, are not without challenges. In this talk, I will present results from our
recent text mining and curation projects and discuss these challenges. In searching for
other ways to overcome these obstacles, I encourage the community to rethink the roles
that authors of phenotypical information can play in the production of computable data
and possibly the ontologies.
Concurrent V: Microclimate effects on plant phenotype Saturday, February 17, 2018 | 2:30 PM – 5:30 PM
Uncovering the genetic basis for natural variation of root system dynamics
in Arabidopsis
Therese LaRue, Stanford University
Thérèse LaRue, Heike Lindne;Ankit Sirinivas; Guillaume Lobet; and José Dinneny
Carnegie Institute
As the interface between the soil environment and the rest of the plant, root systems play
a key role in determining plant growth. The distribution of roots, termed root system
architecture (RSA), is influenced by the surrounding environment. GLO-Roots (Growth
and Luminescence Observatory for Roots) is a new soil-based root imaging technology,
which enables detailed observations of Arabidopsis thaliana root system growth in soil.
Using this system, we are characterizing how the root systems of a diverse population of
Arabidopsis accessions grow over time and will perform a genome wide association study
to identify common alleles involved in RSA control. In parallel, modelling soil-root
interactions to predict RSA function and performance under different stress conditions
will inform us about improved RSA strategies. Together, this work will investigate how
plant root systems are distributed spatially within the soil and identify ways plants
regulate root system growth to cope with
Evaluation of Plant Environmental Stress Response using “RIPPS”, an
Automated Phenotyping System
Miki Fujita, PhD, RIKEN CSRS
Miki Fujita – RIKEN; Kaoru Urano – RIKEN; Takanari Tanabata – Kazusa Inst.; Saya
Kikuchi – RIKEN; Kazuo Shinozaki – RIKEN
High-throughput and accurate measurements of plant traits facilitate the understanding
of gene function. Especially, with recent advances in quantitative genomics such as QTL
or GWAS, there is a growing need for precise quantification of multiple traits in plants.
However, in the case of environmental stress responses such as drought, it is difficult to
quantify the adaptive responses because multiple environmental factors are intricately
involved in the phenotype. Therefore, precise control of growth conditions is of great
importance to evaluate plant responses to environmental stresses. Recently we have
developed an automatic phenotyping system that evaluate plant growth responses to a
wide spectrum of environmental conditions. The system named RIPPS (RIKEN Plant
Phenotyping System) controls individual soil moisture in continuously rotating 120 pots
by a combination of automatic weighing and watering systems that enable the precise
control of soil water condition is necessary for quantifying the adaptive responses to
environmental stresses such as limited water conditions. RIPPS also take image of top
and side view of the plants every two hours. In this presentation, we’ll demonstrate the
utility of the RIPPS in evaluating drought or salinity tolerance and water use efficiency.
High-throughput 3D analysis of barley shoots reveals novel QTL involved in
leaf growth under salt
Bettina Berger, Australian Plant Phenomics Facility - University of Adelaide
Ben Ward – University of Adelaide; Helena Oakey – University of Adelaide; Chris Brien
– University of Adelaide; Allison Pearson – University of Adelaide; Sonia Negrao – King
Abdullah University of Science and Technology; Rhiannon Schilling – University of
If we want to improve crop productivity in a changing climate, we need to understand
plant growth, stress responses and the underlying genetic control. While we now have
phenotyping tools to measure plant growth at high-throughput in forward genetics
screens, we often just scratch the surface by treating the plant shoot as a single object, not
taking into account its individual components.
To be able to dissect the shoot of small grain cereals, such as wheat and barley, into
individual leaves, we have developed a novel computer vision approach. With only a small
number of images we were able to recover the structure of the shoot, the number of leaves,
their length and growth over time. With few images as input, the 3D shape of the plant
may be ambiguous. To overcome this issue, we used multiple view geometry and prior
knowledge of plant structure to generate plausible models of the shoot. We then applied
this method to screen a bi-parental barley population under low and high soil salinity. In
addition to previously characterised loci related to plant development, we also identified
novel QTL for individual leaf expansion under salinity.
The trait components that constitute whole plant water use efficiency are
defined by unique, environmentally responsive genetic signatures in the
model C4 grass Setaria
Max Feldman, Donald Danforth Plant Science Center
Max Feldman – Donald Danforth Plant Science Center; Patrick Ellsworth – Washington
State University; Asaph Cousins – Washington State University; Ivan Baxter – USDA-
ARS
The complex relationship between plant growth and water use is largely determined by
genetic factors that influence both the morphological and biochemical characteristics of
plants. Improving the efficiency by which plants utilize water is an important breeding
objective that can be translated to improve productivity in agriculture while
simultaneously making it a more sustainable endeavor. To assess the genetic basis of
water use efficiency and trait plasticity, we have utilized high-throughput phenotyping
platform and mass spectrometry to quantify plant size, evapotranspiration and stable
isotope composition of an interspecific Setaria italica x Setaria viridis recombinant inbred
line population in both a well-watered and water-limited environment. Our findings
indicate that measurements of plant size and water use in this system are strongly
correlated. We used a linear modeling approach to partition the traits into the predicted
values of plant size given water use and deviations from this relationship at the genotype
level. The resulting traits describing plant size, water use, water use efficiency and δ13C
are all substantially heritable, responsive to soil water potential differentials and provide
a framework to understand the components of plant water use efficiency. Biparental
linkage mapping successfully identified several pleiotropic loci that exhibit medium-to-
large effects on most traits in addition to many smaller effect loci associated with fewer
traits or specific to well-watered or water-limited environments. This study is the first
report characterizing the genetic architecture of water use efficiency in the model C4
species Setaria and mechanistically links measurement of water use efficiency with δ13C
through several common large effect QTL.
Purdue's New Automatic Phenotyping Greenhouse with Micro-climates
Removed
Jian Jin, PhD, Purdue University
Purdue University
Purdue University deployed a new fully automatic phenotyping greenhouse in May 2017.
This facility is featured for (1) Continuous scanning of each crop plant for up to 20
times/day; (2) Clearly removing the micro-climates impact (the variance of
environmental conditions caused by distribution of lighting, temperature, airflow and so
on across the greenhouse space); (3) Advanced hyperspectral imaging system and data
modeling for plant physiological features predictions. Dr. Jin will also share his view of
next generation plant phenotyping in the next 10 years.
Should Soil Water Availability considered in plant phenotyping for abiotic-
tolerance, and how?
Rony Wallach, Prof., Hebrew University of Jerusalem
Rony Wallach, Prof. – Hebrew University of Jerusalem, Israel
The fundamental mechanism of water flow in plants has been described for many years.
Plants are subjected to atmospheric water demand on one hand and soil water flux into
the roots that should compensate for the demand. How far plants are able to sustain their
leaf water demand is therefore largely dependent on the hydraulic properties of the soil-
root system that depends, inter alia, on the soil water content.
As long as soil hydraulic conductivity do not limit the water flow to the rhizosphere, soil
water fully compensates for the atmospheric water demand (potential transpiration rate).
However, when soil water diminishes, the soil hydraulic conductivity turns to be the
limiting factor in water capture in drought prone environments.
By using a newly developed high throughput platform that monitors at a high-frequency
the transpiration rate and soil water flux to the entire root system, the momentary balance
between input and output fluxes was tracked. The commonly used description of
transpiration rate vs. time, is replaced by transpiration rate vs. soil -water content,
yielding a piecewise linear curve. The break point between the two linear lines denotes
the soil water content from which it becomes a limiting factor under the given
environmental conditions, noted as qcrit.
Combining these results with independently measured soil hydraulic properties enables
to transform the change in soil water content to the change in soil hydraulic conductivity,
to be a measure of soil water availability. This approach enables to phenotype different
plants and cultivars for drought tolerance and a succeeding recovery. In addition, given
that periods of drought vary in length, timing and intensity, the current approach enables
to relate different plant and roots traits with the different types of drought. The
application of these results to plant phenotyping, screening, and water management (e.g.
irrigation) will be discussed.
A Machine-Vision Seedling Emergence Assay
Nathan Miller, University of Wisconsin-Madison
Nathan Miller – University of Wisconsin-Madison; Jeff Gustin – University of Florida;
Mark Settles – University of Florida; Edgar Spalding – University of Wisconsin-
Madison
Seedling emergence is a critical stage in the establishment of a successful crop.
Germination and robust seedling establishment are selected traits during the
development of new varieties but with inefficient, largely manual methods. We have
developed an in-lab, soil-based machine vision emergence platform that automatically
measures the emergence profile, including rate, percent, duration, and time to 50%. The
assay is scalable, can accommodate the application of chemical or environmental
treatments, and can be used with different soil types. Each modular unit has a camera
which monitors 168 seedlings and can fit in an environmentally controlled chamber.
Currently the system includes 12 modular units that can monitor 2,016 kernels at a time.
Streaming images are stored and processed by CyVerse’s cyberinfrastructure using a
custom application. This publicly available software measures percent emergence with
2% False Negative Rate and time to 50% emergence within 30 minutes for maize
seedlings. In initial experiments investigating sources of variation in emergence profile,
we found that time to 50% emergence in Mo17 and B73 were remarkably consistent across
seed lots grown in multiple seasons. Comparing 4 field seasons, genetic differences
between these two inbreds explained 33% of the variance while field season explained
only 1.5%, suggesting time until emergence is under genetic control. A larger screen of
40 diverse genotypes ranged from 103 hours to 142 hours with an average variation of 15
hours within a genotype. Our current target is screening 250 RILs of the IBM mapping
population, which we expect to complete in 8 weeks. While current studies focus on
maize, the assay could be used with any large seed with no modification needed.
Concurrent VI: Graduate training in phenomics: an interdisciplinary
adventure
Saturday, February 17, 2018 | 2:30 PM – 5:30 PM
An Introduction to Deep Learning in Plant Phenotyping Without Agonizing
Pain
Jordan Ubbens, MSc, University of Saskatchewan
Jordan Ubbens – University of Saskatchewan; Ian Stavness – University of
Saskatchewan
Deep learning has long dominated the computer vision field as the most powerful family
of techniques for many image-based vision tasks. Due to these successes, reviews have
called for the penetration of deep learning into the plant phenotyping field. The Deep
Plant Phenomics (DPP) software platform is an open-source Python package which offers
deep learning functionality for various image-based phenotyping tasks. In this talk we
demonstrate how to use the pre-trained networks in the DPP package to perform
vegetation segmentation and leaf counting, as well as how to train and deploy your own
regression models for measuring phenotypic traits of interest. Advanced topics will be
covered including how to design a network using the tool, as well as how to monitor and
interpret the training process. We will also give a brief overview of the state of deep
learning in plant phenotyping and discuss current issues unique to applications in plant
phenotyping.
Teaching students to use supercomputers for phenomics
Eric Lyons, Dr, University of Arizona
Nirav Merchant – University of Arizona
Plant phenomics is transforming plant research. However, a new generation of skills are
required to manage and analyze the hundreds of terabytes of data generated by high-
throughput phenotyping technologies. Applied Concepts in Cyberinfrastructure is a
project based class that takes students from diverse educational backgrounds and teaches
them how to use supercomputers to solve real world problems. Each year, the course
identifies a group of researchers to be the class’ client with a problem in scaling their
computational analyses. Over the course of the semester, the class learns practical
theories behind using different types of high performance, distributed, cloud, and
through computing systems; best practices for using those systems, managing code, and
documentation; and how to work as a team and a team of teams to deliver a solution for
the client. Over the 6 years this course has been taught, the class has worked on problems
from distributed annotation of plant genomes to modeling groundwater movement to
predicting future disease vectoring mosquito abundance to butterfly range maps to
exoplanet detection, with many of these projects becoming peer-reviewed publications.
Each year brings new technologies, new domain areas, and new (unforeseen) challenges.
This talk will discuss the lesson learned by the instructors and how plant phenomics can
benefit by this approach for developing new, scalable solutions for analyzing high-
throughput data.
P3, the Predictive Plant Phenomics Graduate NSF Research Traineeship
(NRT) at Iowa State University
Carolyn Lawrence-Dill, Iowa State University
Carolyn Lawrence-Dill – Iowa State University; Theodore Heindel – Iowa State
University; Julie Dickerson – Iowa State University; Patrick Schnable – Iowa State
University
Modern engineering and data analysis techniques make it feasible to develop methods to
predict plant growth and productivity based on genome and environment information,
however broader skillsets will be needed to unlock this potential, so student training
methods must adapt. This poster describes the structure and activities of a
National Science Foundation Graduate
Research
Traineeship (NRT) award focused on Predictive Plant Phenomics (P3). Our program aims
to increase agronomic output as highlighted by the National Plant Genome Initiative’s
current five-year plan [NST, 2014]. Ph.D. training production levels and types are not
always a good fit for addressing complex technical and societal problems such as these.
To train these scientists, the P3 NRT is using the T-training model proposed by the
American Society of Plant Biology (ASPB) and described in “Unleashing a Decade of
Innovation in Plant Science: A Vision for 2015-2025”. This approach requires that
students get broad exposure to multiple disciplines, work with industry, and develop
effective communication and collaboration skills - all without increasing the time to
graduation. This poster describes how we are working towards meeting these challenges.
Initial results show that the P3 students have more contact with faculty across
departments than single discipline graduate students and are open to learning about new
areas. However, we are still grappling with some issues like finding the best mechanism
for balancing student skills through leveling activities such as boot camps and
introductory course training early on in the program. To learn more about the P3 NRT,
visit: https://www.predictivephenomicsinplants.iastate.edu.
Developing the Pipeline of Plant Phenomics Experts at the Wheat and Rice
Center for Heat Resilience
Argelia Lorence, PhD, Arkansas State University
Argelia Lorence – Arkansas State University
The Wheat and Rice Center for Heat Resilience (WRCHR) is an NSF-funded collaborative
project among the University of Nebraska-Lincoln, Arkansas State University, and Kansas
State University. We are taking a multidisciplinary approach involving plant
physiologists, quantitative geneticists, computational biologists, biochemists, engineers,
informaticians, and precision agronomists to: 1) elucidate the physiological and genetic
basis of high night temperature resiliency of rice and wheat, 2) translate these discoveries
into genetic and phenotypic markers for public and private breeding programs, and 3)
develop a broad continuum for STEM education. Trends at the global-, regional-, and
farm-level point to an increase in minimum night temperatures that is significantly higher
than the rate of increase in maximum day temperatures. Increases in night temperatures
significantly decrease grain yield and quality of rice and wheat, which together provide
over 50% of the caloric intake for humans worldwide. We are building genome to
phenome linkages using automated image-based phenomics approaches in combination
with transcriptomics and metabolomics applied to wheat and rice diversity panels. The
gene and pathways discovered from this approach will be functionally tested for their role
in improving the temperature resilience in rice and wheat. The planned approach
integrates across greenhouse and field scales, captures complex interactions between the
environment and genome during grain development at high spatio/temporal resolution,
and couples genomics and phenomics outcomes within a quantitative, model-based
framework. The broader impacts of the project include: mentoring five early career
faculty; b) an interdisciplinary graduate course for students in plant sciences, agricultural
engineering, computational biology, biochemistry, statistics, and computer sciences with
traditional and online delivery; c) a phenomics boot camp for minority undergraduates,
and d) a hands-on plant phenotyping module that will involve high school students and
teachers from the three participating states.
Reinventing Postgraduate Training in the Plant Sciences through
Modularity, Customization, and Distributed Mentorship
Natalie Henkhaus, PhD, American Society of Plant Biologists
Vanessa Greenlee – Boyce Thompson Institute; Crispin Taylor – American Society of
Plant Biologists; David Stern – Boyce Thompson Institute
The Plant Science Research Network (PSRN), funded by an NSF Research Coordination
Network grant, consists of fourteen professional societies and organizations whose
members are active in plant science research, education, and advocacy. The PSRN has
identified a set of radical recommendations for postgraduate training that emerged from
two workshops held in October 2016 and September 2017. Both workshops were
supported by scenario development, as reported elsewhere, to encourage out-of-the-box
thinking and innovative recommendations. The recommendations call for a cultural shift
that embraces and extends educational delivery trends towards self-learning and distance
learning, considers trainee well-being as an essential requirement for success, and
acknowledges the requirement for two-way communication with the public. This shift is
intended to reinforce a broadening of the STEM workforce in both diversity and numbers,
while continuing to maintain excellence in scientific training. The recommendations are
meant to catalyze pilot programs, and also to build on emergent prototypes that exist
globally. These recommendations broaden and deepen the “T-training” concept
presented in the 2013 publication, Unleashing a Decade of Innovation in Plant Science.
The PSRN’s overarching objective is through engagement of the entire plant science
community; the PSRN members aim to build consensus around research, education, and
training objectives that will promote discovery, broaden participation, and have a
measurable impact on pressing challenges around food, agriculture, the environment,
and human health. Learn more by joining the Plant Science Research Network on Plantae:
https://community.plantae.org/organization/plant-science-research-network/
Help! My data is a out of control! Novel services for management of
distributed phenotypic data
Ramona Walls, University of Arizona
Andrew Magill – Texas Advanced Computing Center; Ming Chen – University of
Arizona; James Carson – Texas Advanced Computing Center; Maria Esteva – Texas
Advanced Computing Center
The integration and management of data for plant phenotyping studies presents multiple
challenges. Many phenotype datasets are big, have multiple contributors, contain
components at different stages of completion, and are stored across different platforms.
Phenotype data are structured in myriad ways, have metadata and identifiers (local or
global) that need to be managed pre- and post-publication, and need to link to data and
metadata for other objects such as specimens, accessions, projects, and publications.
Manually performing data management actions for datasets containing hundreds or
thousands of files is tedious at best, impossible at worst. Solving the genotype-to-
phenotype challenge requires that data be discoverable, trustworthy, interpretable, and
accessible (at least to the creators), no matter where they are located or what stage of
completion they are in. Therefore, a new generation of data management tools is needed
for the kind of big data being generated by plant phenotyping studies. Identifier Services
(IDS) is an Early-concept Grants for Exploratory Research (EAGER) project that is
exploring technical solutions to managing large, distributed data. IDS developed a
number of proof-of-concept micro-services for scientists to register their data, organize
and describe them with metadata, and run checks for data identity, authenticity, and
integrity. IDS records the relations among data components, including those stored
across repositories and storage platforms and in different stages of completion. These
relations provide a representation of the dataset, based on a simple generic data model
that can be adjusted to represent different types of research. The data model in turn
supports visualization and management of large datasets, through tasks like bulk
metadata upload and dataset creation based on queries. Together with community data
standards, the micro-services provided by IDS trace data provenance and establish
authenticity over time, making researchers’ lives easier and supporting reproducible
science.
Who is Phenome 2018? Our journey delivering the digital phenotyping
revolution through a combined focus on technology and people.
Bobby Brauer, Monsanto
Jenna Hoffman – Monsanto; Matt McCown – Monsanto; Roger Weyhrich – Monsanto
Recent decades have witnessed great changes in agriculture through advances in the
system of germplasm, traits, chemistry and biologics - leveraged by growers to produce
sustainable crop yields. Improvements in technologies such as high throughput
genotyping have greatly accelerated our ability to discover these components while
innovations in phenomics are beginning to allow us to measure high quality field
phenotypes at scale. Historically, field phenotyping has been the domain of experts
walking plots with pad and pen, or more recently, experts walking plots with mobile
computers. Environmental metrics have been of a coarse spatial resolution that is
challenging to relate to small research plots. In addition, sensing technologies to quantify
temporally changing phenotypes and environmental factors have met with significant
cost and technological barriers to scale - both for the sensors themselves as well as for
data acquisition & retrieval. That is all changing, however, and quickly. We can now
entertain the imminent prospect of completely reinventing how we manage field testing,
phenotyping, and characterization of agricultural products – with technology at the front
lines. This revolution at Monsanto is only made possible by a collaboration of
technologists, engineers, breeders, agronomists and data scientists to deliver scalable,
scientifically validated, highly usable solutions that truly enhance field R&D. This talk
will describe some of the key principles of this revolution, its enabling technologies, and
the culture shifts that we are creating to bring about the future of high throughput, high
resolution phenotyping and field characterization.
Poster Abstracts
Data Computation, Analysis, Modeling
Saguaro Cactus - 1
Peter Pietrzyk, University of Georgia
Automated phenotyping of root hair traits from microscopy images
Chartinun Chutoe – Mahidol University; Patompong Saengwilai – Mahidol University;
Alexander Bucksch – University of Georgia
Improving nutrient and water uptake in crops is one of the major challenges to sustain a
fast-growing population that faces increasingly nutrient limited soils. Root hairs, which
are specialized epidermal cells, compromise up to 70% of the total root surface area.
Therefore, it is likely that root hairs are important for nutrient and water uptake from the
soil. Microscopy provides a mean to record root hairs as digital images. However,
quantifying root hairs in microscopy images remains a bottleneck because of their
geometry and their complex spatial arrangement. We present a method to automatically
quantify phenotypic traits of root hairs in digital microscopy images. Our method uses a
machine learning approach that classifies root hair, parent root and the image
background. In that way, we resolve complexities of root hairs that may cross each other
or form blobs of two or more hairs. We define metrics to distinguish complex cases
computationally. As a result, we measure the root hair traits, length, number and
orientation. We demonstrate our method on examples of rice and maize under phosphor,
nitrogen and potassium stress. Preliminary results suggest that our method reliably
distinguishes between genotypes and treatments on the basis of the extracted traits. We
believe our study paves a way towards identifying the genetic control of root hair traits
and increased agricultural production in future.
Saguaro Cactus - 2
Valerie Cross, Purdue University
Utilizing Hyperspectral Imaging to Predict Relative Water Content in
Sorghum
Valerie Cross – Purdue University; Jian Jin – Purdue University; Mitch Tuinstra –
Purdue University
Non-destructive, high throughput phenotyping has become one of the key goals in
agriculture research. Quickly measuring or predicting plant traits is important for
improving the breeding process and management of crops. Hyperspectral imaging has
potential to improve the speed, accuracy, and applicability of high throughput
phenotyping. We are using hyperspectral imaging to measure and predict the nitrogen
and water content of sorghum under drought and low-nitrogen conditions in a
greenhouse. This current study was developed using three genotypes of sorghum under
four different treatments, two each of water and nitrogen. The plants were imaged and
ground truth data points, including relative water content, nitrogen content, chlorophyll
content, and biomass, were collected from each plant on the day of imaging. From the
collective hyperspectral imaging data, partial least squares models were developed to
correlate the hyperspectral images with the ground truth data. This resulted in partial
least squares models that predict relative water content and nitrogen content in three
sorghum cultivars.
Saguaro Cactus - 3
Sruti Das Choudhury, University of Nebraska-Lincoln
Holistic and Component Plant Phenotyping Analysis using Visible Light
Image Sequence
Sruti Das Choudhury – University of Nebraska-Lincoln; Ashok Samal – University of
Nebraska-Lincoln; Tala Awada – University of Nebraska-Lincoln
Image-based plant phenotyping facilitates the extraction of observable traits by analyzing
large number of plants in a relatively short period of time with little or no manual
intervention. The emergence timing, total number of leaves present at any point of time
and the growth of individual leaves of a plant during vegetative stage life cycle are the
significant phenotypic expressions that best contribute to assess the plant health. Imaging
techniques have the potential to compute advanced phenotypes by considering whole
plant as a single object (holistic phenotypes) as well as its individual components, i.e.,
leaves and stems (component phenotypes), which provide valuable insights into the
physiological characteristics of the plants regulated by genotypes. We introduce three
holistic phenotypes, namely, plant aerial density, bi-angular convex-hull area ratio and
plant aspect ratio to respectively provide information on biomass, plant rotation due to
shade avoidance and canopy architecture. A novel method to automatically detect the
individual leaves and stem of a maize plant by analyzing 2-dimensional visible light image
sequences captured from the side using a graph theoretic approach is introduced. The
total number of leaves are counted and the length of each leaf is measured for all images
in the sequence for automated leaf growth monitoring. We also introduce a set of new
component phenotypes, namely stem angle, inter-junction distance, junction-tip
distance, leaf-junction angle, integral leaf-skeleton area and leaf curvature, with
significance in plant science. To evaluate the performance of the proposed algorithm, a
benchmark dataset is indispensable. Being inspired by the unavailability of such a dataset,
we introduce University of Nebraska-Lincoln Component Plant Phenotyping Dataset
(UNL-CPPD) and provide ground truth to facilitate new algorithm development and
uniform comparison. Detailed experimental analyses are performed on UNL-CPPD to
demonstrate the temporal variation of the component phenotypes in maize regulated by
genotypes.
Saguaro Cactus - 4
David LeBauer, PhD University of Illinois
TERRA REF Open Software, Data, and Computing to Advance Phenomics
Max Burnette – National Center for Supercomputing Applications; Rob Kooper –
National Center for Supercomputing Applications; Craig Willis – National Center for
Supercomputing Applications; TERRA REF Team – University of Arizona, Danforth
Center, Washington University St. Louis, St. Louis University, George Washington
University, Kansas State University, USDA, University of Illinois, National Center for
Supercomputing Applications
Automated measurements have the potential to advance science and agriculture.
However, there are many technical and economic barriers to entry for scientists. Sensors
and sensing platforms are expensive and difficult to use; software used to process and
interpret these data streams can be expensive and inflexible - if it exists. We need the
ability to build, extend, and combine databases and software components into novel
pipelines.
The TERRA Phenotyping Reference Platform (TERRA-REF) team is developing modular,
reuseable phenomics. We are also creating a large and heterogeneous reference data set
for field and controlled-environment phenotyping platforms. Finally, we have cloud
based development environments that allow users to develop, evaluate, and share
algorithms.
This poster will describe our design approach that enables integeration and development
of modular, interoperable, and extensible components. We will also describe how
software components can be reused, improved, and created. We want to promote and
facilitate the sharing data and tools within the phenomics community so that scientists
cans spend more time on discovery.
Saguaro Cactus - 5
Tyson Swetnam, CyVerse BIO5 Institute University of Arizona
Portable, scalable, high throughput geospatial analyses with Singularity
containers on cloud and high performance computing.
Mats Rynge – Information Sciences Institute, University of Southern California; Jon
Pelletier – Department of Geosciences, University of Arizona
Reproducible science with geographic information systems (GIS) on cloud, high
throughput computing (HTC), and high performance computing (HPC) requires portable,
scalable, workflows as part of the Research Object. Here we present a method for running
free and open-source software for GIS; i.e. Geospatial Data Abstraction Library (GDAL),
Geographic Resources Analysis Support System (GRASS), and System for Automated
Geoscientific Analyses (SAGA), in tandem with a workflow management system,
Makeflow, on cloud and HPC using Singularity containers. Our example workflow
involves the computation of daily and monthly sum solar irradiation using an OpenMP
version of the GRASS r.sun algorithm. A single virtual machine (VM) masters the
workflow, with remote workers connected over Internet2 started on cloud, HTC, and/or
HPC platforms, all using the same Singularity container. The workflow is currently
deployed on the OpenTopography.org cyberinfrastructure, where users can select any
location on the terrestrial earth surface using national or global digital elevation model
(DEM) data to calculate global irradiation and daily hours of sunlight. Our workflow links
with OpenTopography via the Opal2 toolkit for wrapping this particular scientific
application as a Web service from a XSEDE Jetstream VM. The workers are launched on
demand on XSEDE Comet HPC and Open Science Grid HTC. Importantly, because the
workflow is containerized with Singularity, it can be re-deployed on any combination of
local desktop, cloud, or HTC / HPC by simply pulling the code from our GitHub repository
and following a few basic setup instructions. Containerized workflows such as ours that
take an open science approach, as part of the Research Object, will allow for future
reproducible geospatial science on cyberinfrastructure.
Saguaro Cactus - 6
Ian Braun, Iowa State University
Computational Classification of Phenologs across Biological Diversity
Ian Braun – Iowa State University; Carolyn Lawrence-Dill – Iowa State University
Phenotypic diversity analyses are the basis for research discoveries that span the
spectrum from basic biology (e.g., gene function and pathway membership) to applied
research (e.g., plant breeding). Phenotypic analyses often benefit from the availability of
large quantities of high-quality data in a standardized format. Image and spectral
analyses have been shown to enable high-throughput, computational classification of a
variety of traits across a wide range of phenotypes. However, equivalent phenotypes
expressed across individuals or groups that are not anatomically similar can pose a
problem for such classification methods. In these cases, high-throughput, computational
classification is still possible if the traits and phenotypes are documented using
standardized, language-based descriptions. In the case of text phenotype data, conversion
to computer-readable “EQ” statements enables such large-scale analyses. EQ statements
are composed of entities (e.g., leaf) and qualities (e.g., length) drawn from terms in
ontologies. In this work, we present a method for automatically converting free-text
descriptions of plant phenotypes to EQ statements using a machine learning approach.
In each description, words related to entities and qualities are identified using the
CharaParser annotation tool (Cui, 2012). A classifier identifies potential matches between
these words and terms from a set of ontologies, including GO (gene ontology), PO (plant
ontology), and PATO (phenotype and trait ontology), among others. The features used by
this classifier include semantic, syntactic, and context similarity metrics between words
and ontology terms. This classifier is trained and tested using a dataset of manually
converted plant descriptions and EQ statements from the Plant PhenomeNET project
(Oellrich, Walls et al., 2015). The most likely matching terms identified by the classifier
are used to compose EQ statements. Any obtained results of these automated conversions
in terms of precision and recall will be presented. Potential use across datasets to enable
automated phenolog discovery are discussed.
Saguaro Cactus - 7
Travis Gray, University of Saskatchewan
Beyond Orthomosaics: Multi-Image Spectral Analysis of Agricultural UAV
Imagery
William van der Kamp – University of Saskatchewan; Travis Gray – University of
Saskatchewan; Steve Shirtliffe – University of Saskatchewan; Hema Duddu – University
of Saskatchewan; Kevin Stanley – University of Saskatchewan; Ian Stavness – University
of Saskatchewan; Mark Eramian – University of Saskatchewan
Imagery from Unmanned Aerial Vehicles (UAVs) is frequently used for crop assessment
and phenotyping in agricultural research fields. In particular, spectral indices, such as
NDVI, are commonly employed to estimate traits such as the health, growth stage, and
biomass in crops. In virtually all such studies, many overlapping images from a UAV flight
are first stitched into a single orthomosaic image, and then spectral indices are derived
from the orthomosaic. But this method necessarily discards (or aggregates) much of the
original pixel information. For example, a single plot may appear in 10 or more individual
images from the UAV, but appear only once in the final orthomosaic. In this paper, we
show that an index value extracted from individual calibrated images of the same plot can
deviate by 10% or more from the index value of the corresponding orthomosaic segment.
This could have important consequences for fine-grained comparison of indices. To
address this problem, we propose alternative approaches to estimating indices, each of
which more directly incorporates all of the individual UAV images. We evaluate these
approaches, and compare them with the orthomosaic approach, by analysing weekly
index values from plant breeding experiments for lentil, wheat, and canola crops in
comparison to relevant manually-measured phenotypes.
Saguaro Cactus - 8
Austin Meier, Oregon State University
The Planteome: A Resource for Reference Ontologies for Plants and Plant
Genomics Database
Laurel Cooper – Oregon State University; Austin Meier – Oregon State University; Justin
Elser – Oregon State University; Pankaj Jaiswal – Oregon State University; Mari-
Angelique Laporte – Bioversity International; Elizabeth Arnaud – Bioversity
International; Seth Carbon – Lawrence Berkeley National Laboratory; Chris Mungall –
Lawrence Berkeley National Laboratory; Barry Smith – University at Buffalo; Dennis
Stevenson – New York Botanical Garden
The Planteome project (http://www.planteome.org) is a centralized online plant
informatics portal which provides semantic integration of a large and growing corpus of
plant genomics data with a suite of reference and species-specific ontologies for plants.
The Planteome reference ontologies include the Plant Ontology, Plant Trait Ontology, the
Plant Stress Ontology and the Plant Experimental Conditions Ontology, along with other
reference ontologies developed by collaborators. Species-specific trait ontologies for crop
plants are mapped to the associated terms in the reference ontologies to create an
integrated ontological network. This integration facilitates studies of plant traits,
phenotypes, diseases, gene function and expression, and genetic diversity data across a
wide range of plant species. In addition, collaboration and annotation tools are being
developed, including the Planteome Noctua platform and remotely accessible APIs. All
the Planteome ontologies are publicly available and are maintained at the Planteome
GitHub site (https://github.com/Planteome) for sharing, and tracking revisions and
issues. The associated data files are freely available for download from the project SVN
(http://planteome.org/svn), and also directly from the Annotation search page on the
Planteome portal (http://browser.planteome.org/amigo/search/annotation).
Saguaro Cactus - 9
Miao Liu, The Climate Corporation
Opportunities & Gaps in Science-Driven Insights in Digital Ag
Frank Dohleman – The Climate Corporation
Digital Agriculture is a new and growing area of research and development. Finding
sufficient high quality data to drive insights from measurements and models for growers
provides an exciting opportunity and enables informed, probabilistic decision making to
maximize the productivity, efficiency, sustainability and profitability of their operations.
The adoption of digital agriculture has grown dramatically in previous years, with over
100 million acres mapped in the Climate FieldviewTM platform.
What have been the biggest successes in digital agriculture to date? What are the
opportunities continue to evolve and drive value out of the digital agriculture arena to
help farmers combat the same challenges they've faced for decades? This presentation
will provide insights into the confluence of soil science, plant science and agronomy with
engineering, software development, and data science to help drive smarter, more efficient
agriculture.
Saguaro Cactus - 10
Seyed Vahid Mirnezami, PhD - Iowa State University
High throughput monitoring anthesis progression of field-grown maize
plants
Seyed Vahid Mirnezami – Iowa State University; Yan Zhou – Iowa State University;
Srikant Srinivasan – IIT Mandi; Baskar Ganapathysubramanian – Iowa State University;
Patrick Schnable – Iowa State University
The tassel is the male organ of the maize plant. Sufficient pollen production is crucial for
the production of hybrid seed. Good seed set requires both sufficient daily production of
pollen, but also pollen shed on enough days to ensure a good “nick” with the receptivity
of female inbreds. Traditional approaches for phenotyping anthesis progression are time-
consuming, subjective, and labor intensive and are thus impractical for phenotyping large
populations in multiple environments. In this work, we utilize a high throughput
phenotyping approach that is based on extracting time-lapse information of anthesis
progress from digital cameras. The major challenge is identifying the region of the interest
(i.e. the location of tassels in the imaging window) in the acquired images. Camera drift,
different types of weather, including fog, rain, clouds, and sun and additionally, occlusion
of tassels by other tassels or leaves complicated this problem. We discuss various
approaches and associated challenges for object detection and localization under noisy
conditions. We illustrate a promising deep-learning approach to tassel recognition and
localization that is based on Region with Convolutional Neural Network (R-CNN). It is
able to reliably identify a diverse set of tassel morphologies. We subsequently extract
time-dependent tassel traits from these localized images.
Saguaro Cactus - 11
Ethan Stewart, PhD - Cornell
The development and application of a deep learning approach for
quantitative disease phenotyping
Chad DeChant – Columbia; Harvey Wu – Columbia; Tyr Wiesner-Hanks – Cornell; Nick
Kaczmar – Cornell; Rebecca Nelson – Cornell; Hod Lipson – Columbia; Michael Gore –
Cornell
Plant disease is estimated to cause a 13% reduction in global crop production. In order to
breed for improved crop varieties with improved disease resistance, accurate measures of
disease symptoms are required. Traditional visual assessments of disease incidence and
severity are time consuming and prone to human error. Conventional image analysis can
help to improve accuracy and throughput, but requires consistent image conditions that
are difficult to achieve in the field. The advent of deep learning algorithms has helped to
overcome these challenges through training a network that recognizes features of interest
across a diverse range of field environments. Convolutional neural networks (CNN) have
been used to classify images for the presence/absence of one or more diseases. We
previously trained a CNN to recognize foliar lesions caused by Northern leaf blight—a
serious disease of maize—in in UAV-acquired images of field-grown maize plants. We are
now extending this approach by applying instance segmentation using the Mask R-CNN
framework to segment aerial images into 3 classes: leaf, disease lesions and background.
With such an approach it will be possible to provide an aerial based assessment of NLB
disease incidence (qualitative) and severity (quantitative) throughout the maize growing
season.
Saguaro Cactus - 12
Erin Gilbert, MS - University of Minnesota
Hyperspectral phenotyping for early detection of soybean sudden death
syndrome
Erin Gilbert – University of Minnesota; Grace Anderson – University of Minnesota;
James Kurle – University of Minnesota; Cory Hirsch – University of Minnesota
Soybean Sudden Death Syndrome (SDS), a disease caused by Fusarium virguliforme, is
becoming increasingly common in northern latitudes. From 2003 to 2005, SDS was
estimated to be among the five most damaging soybean diseases in the United States, and
it continues to spread nearly unimpeded. Initial infection and fungal development takes
place in the soil, and causes no visible foliar symptoms until late in the growing season,
when symptoms appear during late reproductive stages as interveinal chlorosis and
necrosis accompanied by defoliation. SDS is managed by planting resistant varieties or
application of fungicidal seed treatments. Evaluation for resistance to SDS is typically
conducted as assessment of root rot severity, which requires destructive sampling, or
foliar symptom expression during early vegetative growth approximately 30 days after
planting or during late reproductive stages 60 days or more after planting. In other plant
disease systems, hyperspectral radiometry has been used to detect wavelengths that are
informative for both abiotic and biotic stresses. We are investigating the use of
hyperspectral imaging to assess the response of soybean lines to infection of soybean
seedlings by F. virguliforme to enable earlier and more rapid evaluation of varietal
resistance. Here we demonstrate the use of hyperspectral imagery to detect SDS infected
seedlings in a growth chamber experiment within 20 days of emergence. Using open
source packages, we have automated the identification of plants and pixel wavelength
data extraction. A focus of our analysis is towards understanding changes to detect the
type and timing of stresses. Data obtained through hyperspectral imagery was correlated
with foliar disease and root rot severity evaluated using traditional qualitative methods of
disease assessment.
Saguaro Cactus -13 Suxing Liu, University of Georgia
Put the carbon back into the soil: 3D root phenotyping for improved carbon
sequestration
Suxing Liu – University of Georgia; Alexander Bucksch – University of Georgia
Carbon rich soil ensures the fertile soils and the agricultural productivity of plants that
sequester atmospheric carbon available as CO2 into the soil. Deeper rooting crops are the
key to increase carbon content below ground and to improve soil quality and agricultural
output. Root phenotyping is crucial since it provides avenues to quantify deeper root traits
of important crops like maize. However, due to the opaque nature of soil, dense and highly
occluded maize root system, quantifying these traits such as whorl number, distance and
number of crown roots is very challenging.
We developed a 3D reconstruction method that completely digitizes the maize root
architecture. The classic structure from motion algorithm was not able to reconstruct
detailed inner roots architecture without manual registration, we extends this method to
produce a dense point could root model based on images collections taken only from the
outside of roots. Our extensions allow automated reconstruction and measurement of the
inner occluded root system.
We demonstrate the quality of our method on two maize genotypes with six replicates for
each genotype. Our 3D root model reconstruction method is a first promising step
towards automated quantification of highly occluded maize root system. And it enable the
discovery of genes associated with deeper rooting by molecular biologists and pave a
promising way to increase soil carbon sequestration in crops<./p>
Eagle Claws Cactus - 14
Liao Fuqi, MA Noble Research Institute
Plant Root Quantitative Analysis
Fuqi Liao, MS – The Noble Research Institute
Root quantitative analysis has become more and more important in plant research.
Currently, many biologists manually measure the root length with help of software (for
example, WinRhizo), but it is limited to small numbers of roots and costs a lot of time.
Many research projects (for example, GWAS) need to quantify a large number of root, and
require software to detect and process root images automatically and finish analysis in
short time. In many situations, precise measurements could be made by software, instead
of manual analysis. Here, we report development of a series of software, which automated
root image analysis with parallel computing on High Performance Computing (HPC)
cluster. The software have been applied to thousands of images of Arabidopsis and
Medicago truncatula roots. With many parameters, which quantifying the roots, the
automated feature of the software allows analysis of thousands of root samples within a
short period of time.
The software can detect nearly 90% of the root hairs, whose length is less than one
millimeter, and measures the root hair lengths. For roots, which were grown for two
weeks in the lab, the software can detect tips of lateral roots, and analyze the lengths of
main root, and multi-layers of lateral roots. The software also quantifies the roots from
the field with many parameters, including root area, root angle, and total length, etc.
The series of software include many algorithms to provide precise detection and
measurement of the root’s features. In addition, it includes statistical methods, such as
ANOVA and 95% CI, to find significant difference among root groups with genotypes such
as natural variants and developmental mutants. The artificial neural network will soon be
added to classify root groups<./p>
Data Crunching and New Analytics
Eagle Claws Cactus - 15
Amy Tabb, PhD - USDA-ARS-AFRS
Phenotyping tree shape in the field using computer vision and robotics
Phenotyping of tree shape is a challenging problem, not least of which because the
traditional metrics of tree shape – height, width, branch number, branch angle, branch
diameter, and branch length, may not be particularly characteristic of the structural
differences that are evident to humans between phenotypes. We describe ongoing work
to develop a robot vision system that captures the above metrics of fruit tree shape
autonomously and accurately, as well as complete tree reconstructions for use in novel
shape descriptors. I will demonstrate how the system operates in field settings, and
describe its constraints and possible applicability to other species.
Education & Outreach
Eagle Claws Cactus - 16
Carolyn Lawrence-Dill, Iowa State University
P3, the Predictive Plant Phenomics Graduate NSF Research Traineeship
(NRT) at Iowa State University
Carolyn Lawrence-Dill – Iowa State University; Theodore Heindel – Iowa State
University; Julie Dickerson – Iowa State University; Patrick Schnable – Iowa State
University
Modern engineering and data analysis techniques make it feasible to develop methods to
predict plant growth and productivity based on genome and environment information,
however broader skillsets will be needed to unlock this potential, so student training
methods must adapt. This poster describes the structure and activities of a
National Science Foundation Graduate
Research
Traineeship (NRT) award focused on Predictive Plant Phenomics (P3). Our program aims
to increase agronomic output as highlighted by the National Plant Genome Initiative’s
current five-year plan [NST, 2014]. Ph.D. training production levels and types are not
always a good fit for addressing complex technical and societal problems such as these.
To train these scientists, the P3 NRT is using the T-training model proposed by the
American Society of Plant Biology (ASPB) and described in “Unleashing a Decade of
Innovation in Plant Science: A Vision for 2015-2025”. This approach requires that
students get broad exposure to multiple disciplines, work with industry, and develop
effective communication and collaboration skills - all without increasing the time to
graduation. This poster describes how we are working towards meeting these challenges.
Initial results show that the P3 students have more contact with faculty across
departments than single discipline graduate students and are open to learning about new
areas. However, we are still grappling with some issues like finding the best mechanism
for balancing student skills through leveling activities such as boot camps and
introductory course training early on in the program. To learn more about the P3 NRT,
visit: https://www.predictivephenomicsinplants.iastate.edu.
Eagle Claws Cactus - 17
Jordan Ubbens, MSc - University of Saskatchewan
An Introduction to Deep Learning in Plant Phenotyping Without Agonizing
Pain
Jordan Ubbens – University of Saskatchewan; Ian Stavness – University of Saskatchewan
Deep learning has long dominated the computer vision field as the most powerful family
of techniques for many image-based vision tasks. Due to these successes, reviews have
called for the penetration of deep learning into the plant phenotyping field. The Deep
Plant Phenomics (DPP) software platform is an open-source Python package which offers
deep learning functionality for various image-based phenotyping tasks. In this talk we
demonstrate how to use the pre-trained networks in the DPP package to perform
vegetation segmentation and leaf counting, as well as how to train and deploy your own
regression models for measuring phenotypic traits of interest. Advanced topics will be
covered including how to design a network using the tool, as well as how to monitor and
interpret the training process. We will also give a brief overview of the state of deep
learning in plant phenotyping and discuss current issues unique to applications in plant
phenotyping.
Eagle Claws Cactus-18
Zheng Xu, PhD - University of Nebraska-Lincoln
CT image-based Segmentation and Reconstruction of Root Systems by
Machine Learning and Computational Methods
Camilo Valdes – Florida International University; Stefan Gerth – Fraunhofer Institute for
Integrated Circuits IIS; Jennifer Clarke, Sleep – University of Nebraska-Lincoln
Computed Tomography (CT) scanning technologies have been widely used in many
scientific fields, especially in medicine and materials research. A lot of progress has been
made in agronomic research thanks to CT technology. CT image-based phenotyping
methods enable high-throughput and non-destructive measuring and inference of root
systems, which makes downstream studies of complex mechanisms of plants during
growth feasible. An impressive amount of plant CT scanning data has been collected, but
how to analyze these data efficiently and accurately remains a challenge.
We present new computational and machine learning methods for better segmentation
and reconstruction of root systems from 3D CT scanning data. We propose new
approaches within the category of voxel thresholding methods. Considering special
characteristics of root systems, we propose our methods based on two new local-feature
statistics, i.e., proportion and weighted proportion. We found that methods based on our
two new statistics can calibrate root system magnitudes faster than traditional vessel-
based approaches while preserve similar levels of performance. In addition, we propose
and evaluate machine learning approaches in root-system segmentation and
reconstruction from CT-images, in particular simulation-assisted machine learning
approaches. We illustrate and compare different approaches using both simulated and
real CT scanning data from Fraunhofer Institute for Integrated Circuits IIS.
Eagle Claws Cactus - 19
Natalie Henkhaus, PhD - American Society of Plant Biologists
RCN: Coordinated Plant Science Research and Education Network
Vanessa Greenlee – Boyce Thompson Institute; Crispin Taylor – American Society of
Plant Biologists; David Stern – Boyce Thompson Institute
The Plant Science Research Network (PSRN), funded by an NSF Research Coordination
Network grant, consists of fourteen professional societies and organizations whose
members are active in plant science research, education, and advocacy. The PSRN has
identified a set of radical recommendations for postgraduate training that emerged from
two workshops held in October 2016 and September 2017. Both workshops were
supported by scenario development, as reported elsewhere, to encourage out-of-the-box
thinking and innovative recommendations. The recommendations call for a cultural shift
that embraces and extends educational delivery trends towards self-learning and distance
learning, considers trainee well-being as an essential requirement for success, and
acknowledges the requirement for two-way communication with the public. This shift is
intended to reinforce a broadening of the STEM workforce in both diversity and numbers,
while continuing to maintain excellence in scientific training. The recommendations are
meant to catalyze pilot programs, and also to build on emergent prototypes that exist
globally. These recommendations broaden and deepen the “T-training” concept
presented in the 2013 publication, Unleashing a Decade of Innovation in Plant Science.
The PSRN’s overarching objective is through engagement of the entire plant science
community; the PSRN members aim to build consensus around research, education, and
training objectives that will promote discovery, broaden participation, and have a
measurable impact on pressing challenges around food, agriculture, the environment,
and human health. Learn more by joining the Plant Science Research Network on Plantae:
https://community.plantae.org/organization/plant-science-research-network/
Phenomics Enabled Biology
Eagle Claws Cactus - 20
Md Rofiqul Islam, Gauhati University
Agarwood: DNA fingerprinting to Biomarkers
Md Rofiqul Islam – Gauhati University; Sofia Banu – Gauhati University
Aquilaria malaccensis is a woody plant producing agarwood in it's stem which is highly
valuable resinous fragrant deposits. Agarwood is widely used in traditional medicines,
incense and perfume. The objective of this study was to convert transcript into usable
biomarkers. To achieve this objective, cDNA-AFLP technique was used to identify
transcriptionally regulated genes in A. malaccensis. Samples of wood were collected from
plants showing infection from three different locations of Assam and cDNA were
prepared. cDNA-AFLP analysis involved selective amplification with 64 different pair of
primers that allowed the visualisation of 2760 reliable differentially expressed transcript
derived fragments (TDFs).Of these 30 different TDFs were successfully cloned and 20
fragments were sequenced. Eight of which have been identified as Aquilaria transcript
using homology search by BLAST, six have been found to be directly involved in terpenoid
pathway. Primers were designed from these TDFs sequence and expression patterns in
infected and non-infected plants were studied using the real time polymerase chain
reaction. All the terpenoid genes TDFs were found to be up regulated in infected Aquilaria
plants as compared to non-infected plants. The study has identified the TDFs that are
overexpressed in infected plants which can be used as biomarkers for distinction of non-
infected from infected plants using very small wood samples from the plant thus it can be
used as a tool for reliable identification of status of infection in Aquilaria prior to
harvesting .
Eagle Claws Cactus - 21
Ramesh Katam, Florida A&M University
Physiological and Proteomic Analysis of Pistachio Rootstocks in Response to
Salinity Stress
Mohammad Akbari – University of Tabriz; Nasser Mahna – University of Tabriz
Pistachio (Pistacia vera L.), cultivated in arid and semi-arid regions, is one of the most
important nuts worldwide. However, the mechanisms underlying salinity tolerance of this
plant is not well understood. Hence, the studies were carried out both physiological and
molecular level to unravel the metabolic pathways associated with the salt tolerance
mechanisms in various cultivars. Five one-year-old pistachio rootstocks were treated with
four saline water regimes (control, 8 dS m-1, 12 dS m-1, and 16 dS m-1) for 100 days and
physiological, biochemical and proteomic analysis were carried out. Salinity decreased
the Relative water content, Total chlorophyll content and carotenoids in the leaves, and
ascorbic acid and total soluble proteins in both leaves and roots.
Results shows that three different ion exclusion strategies were observed in studied
rootstocks, (i) Na+ exclusion in UCB-1, because retained most of its Na+ in the roots (ii)
Cl- exclusion in Badami, which most of its Cl- remained in the roots (iii) and similar
concentrations of Na+ and Cl- were observed in the leaves and roots of Ghazvini, Akbari
and Kale-Ghouchi. Based on the results, rootstocks arranged from tolerant to susceptible
follows: UCB-1 > Badami > Ghazvini > Kale-Ghouchi > Akbari. High throughput
comparative proteomics of roots identified 153 upregulated and 69 downregulated
proteins in UCB-1 (as tolerant) and 340 upregulated and 18 downregulated proteins in
Akbari (as susceptible). The majority of identified proteins have the functions related to
stress responsive proteins, signal transduction, cell wall and cytoskeleton metabolism,
and transcription factor. The data suggests a strong linkage of molecular mechanism with
the physiological traits in the cultivars with various salt tolerances, and lead to further
functional elucidation and genetic engineering approaches to improve salt tolerance in
plant species.
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Raksha Singh, University of Arkansas Fayetteville
Unraveling the complexity of callose deposition during plant immunity by
integrating two new players: AtNHR2A and AtNHR2B
Laura Ortega, Undergraduate – University of Arkansas; Clemencia Rojas, Assiatant
Professor – University of Arkansas
Callose deposition occurs throughout plant development and in response to potential
fungal, oomycete and bacterial pathogens. Callose biosynthesis is linked to glucosinolate
metabolism and requires the plant hormone ethylene and the genes PEN2 (penetration
2), a myrosinaseand PEN3 (penetration 3), an ABC transporter. Although callose
biosynthesis has been elucidated, little is known about the cellular events mediating its
deposition to the cell wall. We have identified two genes in Arabidopsis thaliana:
AtNHR2A (A. thaliana nonhost resistance 2A) and AtNHR2B (A. thaliana nonhost
resistance 2B) as new components of the plant innate immune system. AtNHR2A and
AtNHR2B play essential roles in callose deposition in response to the bacterial pathogen
Pseudomonas syringae pv tabaci. Using laser scanning confocal microscopy, we showed
that fluorescent versions of AtNHR2A and AtNHR2B: RFP-AtNHR2A and AtNHR2B-
GFP localize to the cytoplasm and to small and dynamic subcellular structures
reminiscent of the endomembrane system. Interestingly, genetic analysis of Atnhr2b x
Atnh2a double mutant together with the finding that both RFP-AtNHR2A and AtNHR2B-
GFP partially co-localize indicate that both proteins have a synergistic function in callose
deposition. Genetic analysis of the double mutants: Atpen2 X Atnhr2a, Atpen2 X
Atnhr2b, Atpen3 X Atnhr2a, Atpen3 X Atnhr2b showed that AtNHR2A and AtNHR2B
are components of the PEN2/PEN3 pathway. While the identity of the subcellular
compartments where AtNHR2A and AtNHR2B localize is still unknown, our findings
suggest that these compartments participate in the delivery of callose to the cell wall and
thus, this work provides fundamental knowledge regarding the poorly understood
phenomenon of cell wall modification during bacterial infection.
Eagle Claws Cactus - 23
Nathanael Ellis, Donald Danforth Plant Science Center
Identifying root architectural differences in field excavated crown roots
between density adapted populations of maize
Nathanael Ellis – Donald Danforth Plant Science Center; Kari Miller – Donald Danforth
Plant Science Center; Jode Edwards – USDA-ARS Corn Insects and Crop Genetics
Research Unit, Ames, IA; Christopher Topp – Donald Danforth Plant Science Center
Since the early 1900’s, annual gains in U.S. maize production have largely been driven by
developing inbred lines for crossing to produce hybrids. Two historically important maize
populations, Iowa Stiff Stalk Synthetic (BSSS) and Corn Borer Synthetic (BSCB), have
been recurrently selected for increased hybrid grain yield, from which many elite
commercial lines have arisen. Yield increase, over this 70-year experiment, is largely
attributed to the selection of maize improved performance in progressively dense
environments. Studies have shown structural and biological differences between
historical and modern maize for above ground traits, but only few experiments focus on
the hidden-half below in similar populations. Here, we compare historical (C0) and
modern (C17) synthetic lines and their hybrids, measuring whole crown roots (CRs) over
time to examine growth rate, dry-weight, and root architecture. RCs were analyzed with
2D root imaging software. BSSS and BSCB C0 populations had consistently larger Crown
Root Area (CRA) compared to C17 synthetic populations, in both 4” dense planting and
12” sparse planting. CRA for each hybrid line depended on cycle and density, Hybrid C0
was larger than parents when sparse but smaller when grown dense. Hybrid C17 CRA was
larger compared to parents in both sparse and dense, and slightly more than C0 in dense.
Hybrids were grown in dense mono- or polyculture rows and 2-D imaged from two angles
to identify difference in biomass distribution. More prominently at flowering, the CR
width of C17 was wider, allocating biomass away from neighbor plants and narrow
between neighbors, regardless neighbor cycle. These results confirm our preliminary data
of major root architectural changes between C17 and C0 in response to density as well as
variation in biomass distribution based on neighboring plants. We hope to narrow down
specific growth rate response at much finer scales, both temporally and spatially.
Eagle Claws Cactus - 24
Florie Gosseau, LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan, France
Heliaphen, an outdoor high-throughput phenotyping platform designed for
the whole plant cycle.
Florie Gosseau – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan, France;
Nicolas Blanchet – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan,
France; Louise Gody – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan,
France; Pierre Casadebaig – AGIR, ENSAT, INRA, Castanet-Tolosan, France; Nicolas
Langlade – LIPM, Universite de Toulouse, INRA, CNRS, Castanet-Tolosan, France
Heliaphen is an outdoor high-throughput phenotyping platform allowing automated
management of growth conditions and monitoring of plants during the whole plant cycle.
A robot moving between plants growing in 15L pots monitors plant water balance and
phenotypes plant or leaf morphology, from which we can compute more complex traits
such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit.
Here, we illustrate the platform capacities on two practical cases: a genetic association
study for yield-related traits and a simulation study, where we use measured traits as
inputs for a crop simulation model. For the genetic study, classical measurements of
thousand-kernel weight (TKW) were done under water stress and control condition
managed automatically on a sunflower bi-parental population. The association study on
TKW in interaction with hydric stress highlighted five genetic markers with one near to a
gene differentially expressed in drought conditions from a previous experiment. For the
simulation study, we used the SUNFLO crop growth model to assess the impact of two
traits measured in the platform (LE and TR) on crop yield in a large population of
environments. We conducted simulations in 42 contrasted locations across Europe and
21 years of climate data. We identified ideotypes (i.e. genotypes with specific traits values)
that are more adapted to specific growing conditions, defined by the pattern of abiotic
stress occurring in these type of environments.
This study exemplifies how phenotyping platforms can help with the identification of the
genetic architecture of complex response traits and the estimation of eco-physiological
model parameters in order to define ideotypes adapted to different environmental
conditions.
Eagle Claws Cactus - 25
Juniper Kiss, Aberystwyth University
Phylogenetic signal in subgenus Rubus (bramble, blackberry) leaflet shape
using geometric morphometrics
Juniper Kiss – Aberystwyth University; Dániel Knapp – Eötvös Loránd University,
Institute of Biology; Gábor Kovács – Eötvös Loránd University, Institute of Biology;
Michal Sochor – Crop Research Institute; Nigel Cooper – Anglia Ruskin University
Plant phenotypic plasticity and different ways of genetic recombination during clonal and
sexual reproduction make the identification of some plant species difficult. Although DNA
barcoding has revolutionised species identification, polyploidy, hybridisation and
apomixis pose challenges to this process. Subgenus Rubus (brambles, blackberries) is one
of the most taxonomically challenging groups of dicots and their morphology based
classification has not been entirely consistent with their molecular phylogeny. The
definition of bramble species is controversial and is often reliant on leaf and leaflet
characters. Here, we combined geometric morphometrics with molecular analysis.
A total of 230 leaves from 115 specimens were imaged from different environments
(woodland, sandy beach, saltmarsh, grassland) in the UK. We conducted a three-loci
molecular analysis using ITS (internal transcribed spacer) region of nrDNA and two
cpDNA regions, maturase K (matK) and trnL–trnF for 23 representative leaf samples. We
analysed the shape of five-foliate and three-foliate leaves using landmark-based image
analysis. Using Principal Component Analysis (PCA) and Canonical Variate Analysis
(CVA), the leaflet shapes clustered according to the different environments.
Discrimination Analysis (DA) also confirmed that most of the group mean shapes were
highly significantly different (P < 0.001) at different locations, while it was more obscure
when analysed for differences in between bramble series. Using squared-change
parsimony, the molecular phylogeny of the haplotypes was projected into the leaflet
morphospace. Permutation tests suggested the phylogenetic signal in leaflet arrangement
morphology to be statistically significant (P < 0.05). These results suggest that each
haplotype has different shapes in different environments, while the overall shape
differences of haplotypes could be explained by their phylogeny. We suggest a statistically
robust approach to combine morphometric analysis with molecular data to understand
the variability of leaflet shape which could affect the morphology-based classification of
Rubus.
Eagle Claws Cactus - 26
Karsten Nielsen, University of Saskatchewan
My Lentils are Bigger than Your Lentils
Steve Shirtliffe, Ph.D – University of Saskatchewan; Hema Duddu, Ph.D – University of
Saskatchewan; Kirstin Bett, Ph.D – University of Saskatchewan; Menglu Wang, B.Sc. –
University of Saskatchewan
One of the most time-consuming components in plant breeding programs is the process
of phenotyping, or identifying traits of interest in a field environment. This data collection
process is typically carried out using human labor, and consumes considerable time and
money. Analyzed overhead images may instead be used as an equivalent or improved
source of information. A diversity panel of 324 lines of lentil (Lens culinaris Medik.),
arranged in randomized complete block design (RCBD) and consisting of three replicates,
were grown in microplots in two locations in Saskatchewan, Canada in 2016 and 2017. An
additional subset of 6 varieties was grown in 3 locations in Saskatchewan in 2017 for the
purpose of collecting whole-plot biomass once every two weeks from emergence until
maturity. All trials were imaged using ground and/or aerial-based overhead imaging
techniques which produce high resolution images.
This research is ongoing. For final analysis, two-dimensional merged images
(orthomosaics) will first be produced. Then, utilizing Structure from Motion (SfM)
techniques, 3-dimensional point clouds will be constructed and utilized as an estimate of
volume. Volume estimates will be compared with biomass measurements. In partnership
with the University of Saskatchewan’s Department of Computer Science, machine
learning techniques may be utilized to correlate UAV imagery with plot biomass. This has
potential to allow rapid, non-destructive biomass estimations and predictions with high
temporal resolution.
Bonker Hedgehog Cactus - 27
Kyle Parmley, Iowa State University
Machine learning approaches in Soybean Phenomics: Predicting Seed Yield,
Oil and Protein in Contrasting Production Systems
Kyle Parmley – Iowa State University
Genetic improvement of soybean [Glycine max (L.) Merr.] has permitted the expansion
of soybean across a broad geographic region. Past breeding efforts have attempted to
develop highly stable cultivars to deploy across all production systems, but these
genotypes may evade an advantageous genotype by management (G x M) interaction, i.e.,
row width spacing. The development of these production system targeted cultivars will
require continual improvement of yield per acre of soybean, which in turn will be
dependent on the modification of physiological traits. Advances in remote sensing
technologies have enabled rapid measurements of these traits on a temporal and spatial
scale, and therefore are becoming increasingly adopted in advanced breeding systems.
The objective of this study to develop yield prediction models using machine learning
approaches. We used two independent studies with 32 genotypes of the SoyNAM panel
with contrasting treatments: row width spacing (38 and 76 cm) and seeding density (123,
345, 568 x 103 seeds ha-1) from nine environments in replicated tests. Physiological trait
data of hyperspectral reflectance, leaf area index, canopy temperature, light interception,
and chlorophyll content were collected at three time points during the growing season.
Robust in-season prediction models identified informative explanatory varaiables for
seed yield, oil and protein predictions, which and will aide in breeding applications for
contrasting production systems. Preliminary results indicate prediction accuracies were
also similar for remote sensing tools with moderate and high throughput capability
thereby decreasing the temporal requirement for data acquisition. The application of
these approaches enable a mechanistic understanding of yield drivers in contrasting
production systems and enable more informative decision making capability.
Bonker Hedgehog Cactus - 28
Sara Tirado, University of Minnesota
Field Based Phenotypic Platform for Characterizing Maize Growth and
Development
Sara Tirado – University of Minnesota; Nicolai Haeni – University of Minnesota; Volkan
Isler – University of Minnesota; Candice Hirsch – University of Minnesota; Nathan
Springer – University of Minnesota
Advances during the past decade have allowed researchers to link genomic information
to phenotypic information and through this enhance crop productivity. Further progress
in the ability to link genotypes, environments, and phenotypes has been limited by the
accuracy and consistency of measuring traits of agronomic importance on a large field-
based scale. Current field-based phenotyping efforts are time and labor intensive and
therefore hinder the development of large-scale trait datasets at multiple timepoints
throughout a plant’s life cycle. This brings a need for developing fully automated,
inexpensive procedures for objectively measuring plant traits in field settings. We have
developed a procedure for utilizing RGB drone imagery to extract phenotypic traits of
importance, including stand count, plant height, canopy closure, and growth rate. This
platform was used to characterize a maize association mapping population that consists
of 500 diverse inbred lines grown in replicate in Saint Paul, MN in 2017. In total five
flights were conducted throughout the growing season. Results of repeatability across
replicates and variation in growth rates as measured through plant height and canopy
closure will be presented. The application of this technology can be used to deepen our
understanding of how genetic variation and environmental influences shape the traits of
corn plants in the field.
Bonker Hedgehog Cactus - 29
Dalal Alonazy, Florida A&M university
Strong Protein-interactions identified in Drought tolerant Peanut Leaf
Proteome
Ramesh Ramesh – FAMU
In peanut water stress predisposes to pre-harvest fungal infection leading to aflatoxin
contamination. Major changes during water stress include oxidative stress leading to
destruction of photosynthetic apparatus and other macromolecules within cells. To gain
better understanding of the effects on molecular and cellular functions, two peanut
cultivars with diverse drought tolerance characteristics were subjected to water stress
(WS). Leaf samples at different stress intervals were subjected to proteome analysis using
two-dimensional electrophoresis complemented with MALDI/TOF mass spectrometry.
Ninety-six proteins were differentially expressed in response to water stress in both
cultivars. Three proteins: glutamine ammonia ligase, chitin class II and actin isoform B,
were unique to tolerant cultivar. Four proteins: serine/threonine protein phosphate PP1,
choline monooxygenase, peroxidase 43 and SNF1-related protein kinase regulatory
subunit beta-2, which play a role as cryoprotectants through signal transduction and
defense were induced in drought tolerant cultivar following WS. Several of the leaf
proteins that were over expressed in tolerant cultivar to WS were suppressed in
susceptible cultivar. Protein interaction prediction analysis suggests that more proteins
interacting in tolerant cultivar were shown to activate other proteins in directed system
response networks. Interologs of these proteins were found in Arabidopsis and we believe
that similar mechanism might exist in peanut.
Bonker Hedgehog Cactus - 30
Anique Josuttes, University of Saskatchewan, Department of Plant Science
Utilizing Deep Learning to Predict the Number of Spikes in Wheat (Triticum
aestivum)
Steve Shirtliffe – University of Saskatchewan, Department of Plant Science; Curtis
Pozniak – University of Saskatchewan; Duddu Hema Sudhakar – University of
Saskatchewan, Department of Plant Science; Menglu Wang – University of
Saskatchewan, Department of Plant Science
There is potential to use phenotypic traits as selection tools in breeding programs. The
bottleneck lies in obtaining phenotypic measurements. Using image analysis to analyze
phenotypic traits of interest may result in more accurate and representative phenotyping.
The number of wheat spikes is shown to have correlations with end yield. Sixteen diverse
Triticum aestivum varieties were seeded in three locations in 2016 and 2017. The trial is
composed of three reps in a randomized complete block design. Plots are seeded double
wide to allow for destructive phenotypic measurements to take place in one plot and yield
to be measured in the other. The total number of wheat spikes per plot were counted.
Also, measurements that contribute to spike metrics such as: plant number, tiller number,
and kernel weight, were collected. Both ground and aerial platforms were tested in the
trial to gather high resolution images.
Collected images have been subjected to two-dimensional merged images, also known as
orthomozaics. Individual plots that were captured from a zenith view are being analyzed
further using artificial neural networks, in hopes of detecting the number of wheat heads
on a per plot basis. This allows for potential, non-destructive, yield predictions. This
research is on-going.
Bonker Hedgehog Cactus - 31
Therese LaRue, Stanford University
Uncovering the genetic basis for natural variation of root system dynamics
in Arabidopsis
As the interface between the soil environment and the rest of the plant, root systems play
a key role in determining plant growth. The distribution of roots, termed root system
architecture (RSA), is influenced by the surrounding environment. GLO-Roots (Growth
and Luminescence Observatory for Roots) is a new soil-based root imaging technology,
which enables detailed observations of Arabidopsis thaliana root system growth in soil.
Using this system, we are characterizing how the root systems of a diverse population of
Arabidopsis accessions grow over time and will perform a genome wide association study
to identify common alleles involved in RSA control. In parallel, modelling soil-root
interactions to predict RSA function and performance under different stress conditions
will inform us about improved RSA strategies. Together, this work will investigate how
plant root systems are distributed spatially within the soil and identify ways plants
regulate root system growth to cope with
Bonker Hedgehog Cactus - 32
Carolyn Rasmussen, PhD University of California, Riverside
Division plane orientation in plant cells
One key aspect of cell division in multicellular organisms is the orientation of the division
plane. Proper division plane establishment significantly contributes to normal
organization of the plant body. To determine the importance of cell geometry in division
plane orientation, we designed a three-dimensional probabilistic mathematical modeling
approach based on century-old observations: equal daughter cell volume and the
resulting division plane is a local surface area minimum. Predicted division planes were
compared to a plant structure that marks the division site, the preprophase band (PPB).
PPB location typically matched one of the predicted divisions. Predicted divisions offset
from the PPB occurred when a neighboring cell wall or PPB was observed directly
adjacent to the predicted division site, as to avoid creating a “four-way junction”.
Population level modeling accurately predicted ~95% of in vivo cell divisions based on
geometry. This powerful model can be used to separate the contribution of geometry from
mechanical stress or developmental regulation in predicting plant division plane
orientation.
Bonker Hedgehog Cactus - 33
Amritpal Singh, Purdue University
Towards the Development of an Aerial Platform for High Throughput
Phenotyping of Maize
Jieqiong Zhao – Purdue University; Ali Masjedi – Purdue University; Yuhao Chen –
Purdue University; Javier Ribera – Purdue University; Yun-Jou Lin – Purdue University;
Addie Thompson – Purdue University; Edward Delp – Purdue University; David Ebert –
Purdue University; Ayman Habib – Purdue University; Melba Crawford – Purdue
University; Mitchell Tuinstra – Purdue University
Tremendous progress has been made in plant genotyping and sequencing in recent years.
Low-cost, high-throughput genotyping platforms are now available to rapidly
characterize crop germplasm collections and breeding populations. Availability of
abundant cheap molecular markers has facilitated gene/quantitative trait loci (QTL)
discovery through genome-wide association studies as well as selection of genetically
superior candidates via genomic selection. Obtaining accurate plant phenotypes however
has remained a major challenge in plant breeding. Plant phenotyping has not achieved a
similar level of accuracy and throughput as achieved in genotyping. High-throughput
phenotyping (HTP) methods using unmanned aerial vehicles (UAVs) fitted with sensors
can potentiality phenotype a large number of research plots for numerous traits. The
objective of this study was to evaluate a UAV based phenotyping system for its ability to
phenotype maize hybrids for traits that are important for crop improvement. Two
replications of 250 maize hybrids that are a part of Genomes to Fields initiative were
grown at the Agronomy Center for Research and Extension (ACRE) at Purdue University
in 2017. High resolution spatial data was collected using RGB-visible, visible/near-
infrared (VNIR), and shortwave infrared (SWIR) sensors fitted on a DJI M600 flying
platform. Ground reference data was collected for plant height, ear height, stand count,
and end of the season stay-green. Remotely sensed data will be processed to obtain the
estimates of the plant height, stand count, and stay green. Predictive ability of this UAV
based platform will be assessed by correlating the results to the ground reference data.
Successful implementation of the UAV based platform may help the private and public
maize breeding programs to accurately phenotype the maize traits and reduce laborious
phenotyping efforts.
Bonker Hedgehog Cactus - 34
Kamaldeep Virdi, PhD - University of Minnesota
Genetic control of soybean (Glycine max L. Merr.) shoot architecture
Gary Muehlbauer – University of Minnesota; Robert Stupar – University of Minnesota;
Aaron Lorenz – University of Minnesota; Austin Dobbels – University of Minnesota;
Suma Sreekanta – University of Minnesota; Jeffrey Roessler – University of Minnesota
Soybean shoot architecture is a complex phenotype, which can be partitioned into various
measurable traits. Variation in shoot architecture likely influences canopy light
interception, photosynthesis, and source-sink partitioning efficiency, and thus is related
to overall grain yield. Here, shoot architecture-related traits are defined as branch angle,
branch number, branch length, leaf shape and size, petiole length, petiolule length,
canopy coverage, days to flowering, maturity, determinancy, and light penetration
through the canopy. A detailed phenotypic characterization of these shoot architecture-
related traits, their contributions to overall shoot architecture, and their genetic control
is imperative to fully exploit the yield potential of soybean. We examined a set of 400
diverse maturity group 1 soybean accessions to study the natural variation for shoot
architecture-related traits, to identify relationships between traits, and to use association
mapping to identify loci that are associated with shoot architecture traits. The panel was
genotyped with 32,650 SNP markers. To collect phenotype data for shoot architecture-
related traits, we used a combination of high-throughput (drone and ground-based
imagery) and conventional phenotyping platforms. Significant QTL associated with
branch angle, leaf length/width ratio, petiolule length, days to flowering, maturity and
stem termination were detected. In most cases, these QTL overlapped with previously
detected genes or QTL. For example, a leaf length/width ratio QTL on chromosome 20
was found to be coincident with the location of a previously isolated Narrow Leaf (Ln)
gene. Interestingly, we detected a branch angle QTL located on chromosome 19 that
overlaps with QTL associated with canopy coverage and light penetration, suggesting
branch angle is an important determinant of canopy coverage.
Bonker Hedgehog Cactus - 35
Tae-Kyeong Noh, Seoul National University
Early diagnosis of plant responses to osmotic stress using spectral image
analysis
Do-Soon Kim, Seoul National University; Tae-Young Lee – Seoul National University;
Hejin Yu – Seoul National University
The spectral image analysis is expected to be a key technology for high-throughput
phenotyping (HTP) or screening (HTS) but not yet fully applied to HTP/HTS. Therefore,
this study was conducted to establish spectral image analysis system with emphasis on
thermal and chlorophyll fluorescence image analysis for plants grown under osmotic
stresses. Various plants were treated with NaCl and poly ethylene glycol (PEG) at an early
growth stage to induce an abiotic stress. Plant body temperature estimated by thermal
image analysis increased with the extent of abiotic stress, giving negative correlations
with photosynthetic rate. Chlorophyll fluorescence intensity decreased with the extent of
abiotic stress, giving positive correlations with photosynthetic rate. These results suggest
that image-based parameters such as plant body temperature and chlorophyll
fluorescence intensity can replace photosynthesis rate and be used to diagnose crop
tolerance to abiotic stress. Normalization of image-based parameters were able to classify
based on osmotic stress tolerance. In conclusion, our results revealed that the spectral
imaging system established in this study could give biological significance to the crop
images in association with physiological parameters and be a part of HTP and HTS.
Bonker Hedgehog Cactus - 36
Haichao Guo, Noble Research Institute, LLC
Decreasing nodal root number in maize enhances nodal and lateral root
length while increasing shoot biomass when nitrogen is limiting
Haichao Guo – Noble Research Institute, LLC; Anand Seethepalli – Noble Research
Institute, LLC; Larry York – Noble Research Institute, LLC
The root phenome influences crop performance, yet our understanding is limited.
Previous simulations using the structural-functional model SimRoot indicated reduced
nodal root number was a promising target for maize breeding. These simulations were
partially confirmed in field and greenhouse studies relying on natural variation of nodal
root number among recombinant inbred lines. In this study, we directly tested the
hypothesis that reduced nodal root number allows increased elongation of remaining
nodal roots and their laterals, and possibly reduces the competition for resources among
roots. We assessed the influence of nodal root number with a manipulative experiment
by excising roots as they emerge from whorls on plant growth and root system
architecture. The maize genotype used was inbred line B73 and the plants were grown in
150 cm tall mesocosms in high and low nitrogen (N) media. Nodal root number
manipulation treatment levels were excising 0%, 33%, and 67% of all nodal roots as they
emerged over time. An isotopic label of 15N was injected at 143 cm depth to test deep N
capture. At 42 days after planting, the root systems were intensively phenotyped by
washing away media, separating the root system by axial root class, dividing into 30 cm
segments from top to bottom, then scanning with a photo scanner. Root scans were
analyzed using a new C++ software called RhizoVision-Scan. Excising nodal roots
resulted in higher lateral per axial root length, greater total root length, and increased
deeper rooting regardless of N level. Under low N, excising nodal roots significantly
increased plant diameter and plant height, and the two excision levels resulted in an
averaged 50% increase in shoot biomass relative to the 0% excision control. These results
confirm the utility of reduced nodal root number as a promising target for maize, and
possibly other cereals.
Bonker Hedgehog Cactus - 37
David Hanson, University of New Mexico
Rapid gas exchange in the phenomic era
Joseph Stinziano – University of Western Ontario
Phenotyping for photosynthetic gas exchange parameters is limiting our ability to select
plants for enhanced photosynthetic carbon gain and to assess plant function in current
and future natural environments. This is due, in part, to the time required to generate
estimates of the maximum rate of ribulose-1,5-bisphosphate carboxylase oxygenase
(Rubisco) carboxylation, the maximal rate of electron transport, and Rubisco activation,
from the response of photosynthesis to the CO2 concentration inside leaf air spaces. To
relieve this bottleneck, we developed a method for rapid photosynthetic carbon
assimilation CO2 responses utilizing non-steady-state measurements of gas exchange.
Using high temporal resolution measurements under rapidly changing CO2
concentrations, we can collect traditional gas exchange parameters in around 2 minutes.
This is a small fraction of the time previously required for even the most advanced gas
exchange instrumentation. We present how we have applied this method to diurnal
changes in physiology as well as responses to light, CO2, and temperature.
Bonker Hedgehog Cactus - 38
Abdullah A Jaradat, USDA/ARS & University of Minnesota
Forward Phenomics of oat Panicles
There is a growing need for adapted and more productive germplasm to expand oat
production, optimize its yield, improve groat quality, and satisfy farmers and consumers
demand, especially in the Upper Midwest of the US. Oat germplasm, representing
different eco-geographical origins and breeding status, was characterized and evaluated
using field- and laboratory-based forward phenomics. Whole plots were phenotyped at
successive growth stages during three growing seasons using aerial and hand-held
imagery and sensors. Digital and high-throughput data were captured and compiled on
(1) color space descriptors of whole plots during key growth stages, (2) growing degree
days to panicle emergence and maturity; (3) phenotypic and structural traits of panicles
and spikelets; and (4) groat quality. A relational database was mined and statistically
analyzed to (1) cluster the oat germplasm using unsupervised hierarchical clustering
method; (2) identify traits with positive or negative, direct or indirect effect on the panicle
phenome; (3) identify a minimum set of traits which can discriminate between
structurally and agronomically different panicle phenotypes; (4) express groat weight per
plant as a function of stochastic panicle architecture and (5) quantify the effect of panicle
architecture traits that have implications for groat quality. A dynamic custom profiling
procedure was instrumental in quantitatively assessing the importance of, and visually
adjusting structural panicle traits to predict and optimize groat agronomic and quality
traits.
Bonker Hedgehog Cactus - 39
Zoë Migicovsky, Dalhousie University
Rootstock effects on shoot system phenotypes in a ‘Chambourcin’
experimental vineyard
Daniel Chitwood – Independent Researcher; Peter Cousins – E. & J. Gallo Winery; Anne
Fennell – South Dakota State University; Zachary Harris – Saint Louis University; Laura
Klein – Saint Louis University; Laszlo Kovacs – Missouri State University; Misha
Kwasniewski – University of Missouri; Mao Li – Donald Danforth Plant Science Center;
Jason Londo – USDA-ARS; Allison Miller – Saint Louis University
Grafted species such as grapevines are an ideal model for understanding how rootstocks
can impact shoot systems phenotypes. We examined an experimental vineyard in Mount
Vernon, Missouri which includes a common scion (‘Chambourcin’) own-rooted as well as
grafted onto three different rootstocks. The vineyard also includes 3 different irrigation
treatments. Leaf shape and ionomic data was collected in 2014 and 2016, while gene
expression (RNA-seq) was collected in 2016 only. We report the results of analyses
determining the impact that varying rootstocks can have on leaf shape, ionomic profile
and gene expression. Future work will expand sampling to include additional phenotypes
and time points across three years.
Organ Pipe Cactus - 40
Sabrina Elias, University of Dhaka and University of Nebraska Lincoln
Deciphering the association of phenome and gene expression postulating
salt tolerance mechanism in a rice landrace, Horkuch
Sabrina Elias – University of Dhaka, University of Nebraska Lincoln; Taslima Haque –
University of Dhaka, University of Texas at Austin; Samsad Razzaque – University of
Dhaka, University of Texas at Austin; Sazzadur Rahman – Bangladesh Rice Research
Institute; Sudip Biswas – University of Dhaka; Thomas Juenger – University of Texas at
Austin; Harkamal Walia – University of Nebraska Lincoln; Zeba Seraj – University of
Dhaka
Rice production in the salty cultivated soil cannot meet the demand of the overgrowing
population as rice plant and excess salt has a rival relationship. Changes in climate is
worsening the scenario by introgression of excess salt in more and more cultivable lands.
But rice, as a major staple food need to maintain the balance in the production requiring
the need of high yielding salt-tolerant rice. Understanding the mechanism of salt tolerant
landraces with adaptive capability to withstand the harsh environment can give insights
on potential candidate genes for conferring tolerance. In order to do so, the reciprocal
cross of a salt tolerant landrace Horkuch and high yielding but sensitive variety, IR29 has
been analyzed. A set of the reciprocal F2:3 population was genotyped using DArTSeq™
for discovering SNP markers to construct linkage map and manual phenotyped under salt
stress. We have identified expression QTLs (eQTL) combining the genotyping and
RNAseq data of a subset under 150mM salt stress. An image-based non-destructive
automated and continuous phenotyping over 3 weeks of salt stress was carried out on a
selected F3 and F5 sub-populations followed by QTL identification for the digital traits
and relative growth rates from visual image data. Instead of endpoint records, image
analysis over days gave us longitudinal data, which could separate the early and late
responses to salt stress. Combining the phenomics and eQTL data, early growth indices
were found to be enriched with transport, osmotic response etc and the later stages were
enriched with genes associated with growth, carbohydrate metabolism, organ
development etc. The phenome data along with the expression data could give a
comprehensive scenario regarding potential candidates involved in tolerance mechanism.
Organ Pipe Cactus - 41
Tara Enders, University of Minnesota
Computer vision and hyperspectral approaches to document temperature
stress responses in maize seedlings
Susan St Dennis – University of Minnesota; Nathan Miller – University of Wisconsin; Liz
Sampson – University of Minnesota; Edgar Spalding – University of Wisconsin; Nathan
Springer – University of Minnesota; Cory Hirsch – University of Minnesota
Yields of maize may be reduced substantially within the next century due to global climate
change. Understanding how maize varieties respond to temperature extremes will be
instrumental in developing varieties that can withstand future abiotic stresses while still
producing high yield. We are documenting the variation of morphological traits, color,
and hyperspectral signals in maize seedlings in response to abiotic stresses in multiple
maize genotypes over time. Morphological measurements, such as plant height, width,
and area, can help characterize the impact of stresses on growth rates. Color data from
RGB images allows for quantification of physiological changes to stress, such as leaf
necrosis, which varies substantially among maize genotypes. Hyperspectral data may
capture valuable information about how genotypes respond to stress that is unable to be
captured using RGB imaging and could provide early detection of stress responses prior
to other manifestations. Documenting multiple traits across genotypes and growth
conditions will uncover the dynamics of maize responses to changing temperatures and
allow for the discovery of genomic loci that could provide improved tolerance.
Organ Pipe Cactus - 42
Donghee Hoh, MSU-DOE Plant Research Laboratory
The genetic and mechanistic bases of photosynthetic cold tolerance in
legume, cowpea (vigna unguiculata (l.) walp.) via high throughput
environmental phenotyping
Isaac Osei-Bonsu – MSU-DOE Plant Research Laboratory; Jeffrey Cruz – MSU-DOE
Plant Research Laboratory; Philip Roberts – University of California, Riverside; Bao-Lam
Huynh – University of California, Riverside; Oliver Tessmer – MSU-DOE Plant Research
Laboratory; Timothy Close – University of California, Riverside; Linda Savage – MSU-
DOE Plant Research Laboratory; David Hall – MSU-DOE Plant Research Laboratory;
David Kramer – MSU-DOE Plant Research Laboratory
Increasing crop production will require improvements in the efficiency, robustness, and
sustainability of photosynthesis. Among the most critical abiotic stresses that impact
photosynthesis is temperature. Cowpea (Vigna unguiculata (L.) Walp.), an important
protein source worldwide especially in developing countries was chosen as a model crop,
is especially sensitive to high temperature during reproductive development result in
severe crop losses by causing male sterility and fruit abortion. One proposed approach to
minimizing the impact of heat stress is to plant earlier than the normal to avoid extreme
heat stress later in the season. This strategy involves planting at colder temperatures,
which is generally known to retard germination and emergence in Cowpea. A major
concern is how the chilling stress impacts plant performance/yield. The goal of this
research is to identify genes and mechanisms related to chilling stress tolerance. Cowpea
has potentially sufficient genetic variation that we can use to identify quantitative trait
loci (QTL) that can be used to guide plant breeding, and test mechanistic models to
explain this sensitivity. Two sets of recombinant inbred lines (RILs) were phenotyped
using the Dynamic Environment Phenotype Imager (DEPI) and MultispeQ under control,
cold temperature conditions, under lighting conditions that simulate typical daylight. We
found considerable natural variation in the photosynthetic parameters which were used
to identify QTLs specific to cold tolerance that can be exploited for quantitative trait locus
(QTL) mapping and subsequent breeding efforts. Phenotyping and QTL data showed cold
sensitivity is strongly associated with increased photoprotective responses (qE and qI)
that is strongly linked to variations in the chloroplast ATP synthase activity, leading us to
propose a model for low-temperature photodamage that involves control of proton motive
force-induced damage to photosystem II. We also identified potential genetic control
element that could explain the adaptation of certain variants to low temperature.
Organ Pipe Cactus - 43
Salme Timmusk, Uppsla BioCenter, Swedish University of Agricultural Sciences; The
Bashan Institute of Science 1730 Post Oak Court, Auburn, AL 36830, USA
Mineral nanoparticles improve plant growth promoting rhizobacterial
performance
Gulaim Seisenbaeva – Uppsala BioCenter, SLU; Dept. of Molecular Sciences; Lawrence
Behers – Uppsala BioCenter, SLU; The Bashan Institute of Science 1730 Post Oak Court,
Auburn, AL 36830, USA
We work with plant growth promoting rhizobacteria (PGPR) i.e. with the native bacterial
species which commonly are present in agricultural soils. Our strains are isolated from
harsh habitats as we have learned that the isolates from the suboptimal conditions,
coevolved with host plant over long period of time, have considerably higher potential for
plant stress alleviation. We design synthetic root biofilms and investigate their mode of
action taking into account that rhizobacterial properties are dynamic and highly
dependent on root age.
We study a novel use of nanotitania (TNs) as agents in the nanointerface interaction
between plants and colonization of PGPR. The effectiveness of PGPRs is related to the
effectiveness of the technology used for their formulation. TNs produced by the Captigel
patented SolGel approach, characterized by the transmission and scanning electron
microscopy are used for formulation of the harsh environment PGPR strains. Changes in
the seedlings biomass, root architecture and in the density of single and double inoculants
with and without TNs are monitored during two weeks of stress induced by drought salt
and by the pathogen Fusarium culmorum. We show that double inoculants with TNs form
stable biofilms on plant roots. Regression analysis indicates that there is a positive
interaction between seedling biomass and TN-treated second inoculant colonization. We
conclude that the nanoparticle treatment provides an effectual platform for PGPR
rational application via design of root microbial community. How rhizosphere varies
along soil–plant system and to what extent it affects acquisition of water and nutrients?
The model system established provides a basis for systems approach using microscale
information technology for microbiome metabolic reconstruction including identifying
genes contributing to variation in phenotypic plasticity.
These new advancements importantly contribute towards solving food security issues in
changing climates.
Organ Pipe Cactus - 44
Marian Brestic, Slovak University of Agriculture in Nitra, Slovakia
Phenotyping of isogenic chlorophyll-deficient wheat mutant lines in relation
to photoprotection and photosynthetic capacity
Marian Brestic – Slovak University of Agriculture in Nitra, Slovakia; Marek Zivcak –
Slovak University of Agriculture in Nitra, Slovakia; Oksana Sytar – Slovak University of
Agriculture in Nitra, Slovakia; Klaudia Bruckova – Slovak University of Agriculture in
Nitra, Slovakia; Xinghong Yang – College of Life Sciences, Shandong Agricultural
University, Taian, China; Xiangnan Li – Northeast Institute of Geography and
Agroecology, Chinese Academy of Sciences, Changchun, China
Chlorophyll-deficient mutants are characterized by unique changes in content and
composition of light harvesting pigment protein complexes in chloroplast. In our
experiments, we examined light responses and photosynthetic capacity of chlorophyll-
less isogenic mutant lines of hexaploid bread wheat (Triticum aestivum L.) and tetraploid
durum wheat (Triticum durum L.) in comparison to parental lines (WT) in different
growth phases and different environmental conditions. Despite the strong chlorina
phenotype of young plants, the tested mutant lines expressed an ability to adapt to natural
environmental conditions, to grow and provide satisfactory grain yield in field conditions.
A detailed in vivo analysis of photosynthetic parameters enabled to characterize the
photosynthetic phenotype of mutant lines. We observed a higher expression of gene
mutations related to a typical chlorina phenotype in tetraploid durum wheat mutants
compared to the hexaploid accessions. In later growth phases, we observed partial relief
of chlorina phenotype, including photosynthetic pigment composition, CO2 assimilation
rate, plant growth and responses of PSII photochemistry. The shift of the phenotype
towards the wild-type in later growth phases was more evident in bread than in durum
wheat, as well as in plants grown in growth chamber compared to plants grown outdoors.
Low chlorophyll content in leaves of mutant lines limited CO2 assimilation only in early
growth phases; in general, the photosynthetic rate per chlorophyll unit was relatively high
in all mutant lines. In the majority of mutants, we observed a limited photoprotective
capacity. Concluding all, our results show that the chlorophyll-less mutant lines of wheat
represent specific biological models with a diverse leaf traits and photosynthetic
responses, including differences in acclimation capacity and a strong interaction with
crop phenology. In this respect, the isogenic mutant lines represent valuable plant models
for different photosynthetic studies and the crop phenotyping programs. (Supported by
APVV-15-0721, VEGA-1-0831-17 and APVV-SK-CN-2015-0005).
Organ Pipe Cactus - 45
Gokhan Hacisalihoglu, PhD Florida A&M University
Seed Priming Modulates Cold Sensitivity in Maize NAM Parental Inbred
Lines
Gokhan HACISALIHOGLU – Florida Agricultural and Mechanical University,
Tallahassee, FL; J. Gustin – Univ. of Florida; Nathan Miller – University of Wisconsin; S.
Kantanka – Florida A&M University; A.M. Settles – Univ. of Florida
Seedling emergence is an important factor for yield, particularly under challenging
planting conditions. In the US corn belt, maize is planted in early spring, as soon as soil
temperatures are permissive to germination. At that time, temperatures often drop below
normal, which can delay or even kill the seedling. Seed pre-treatments have been shown
to improve germination in cold conditions in crops such as rice and cabbage, but are
largely unpublished in maize. To assess the effects of pre-treatments on early maize cold
tolerance, twenty-seven inbred parents of maize Nested Association Mapping (NAM)
population were primed using a synthetic solid matrix and then tested for cold tolerance
using a soil-based emergence assay. Primed kernels were incubated at 10°C for 5 days,
and then transferred to 24°C for emergence. DSLR cameras were used to capture images
every 30 min to obtain emergence profiles of each seedling. Emergence time was
determined from the time-lapsed images and multiple measures including final
emergence percentage, time to 50% emergence, and emergence rate were extracted for
each genotype. The cold treatment reduced total emergence of several genotypes.
However, priming pre-treatment protected the sensitive genotypes allowing nearly full
emergence. We also used single-kernel near infrared reflectance spectroscopy to
determine seed density, weight, oil, protein, and starch for the kernels prior to planting.
By combining kernel characteristics and emergence time, we found small, but highly
significant correlations between the kernel and early seedling performance.
Organ Pipe Cactus - 46
Trevis Huggins, PhD USDA Agricultural Research Service Dale Bumpers National Rice
Research Center
Association analysis for loci regulating grain quality traits and marker
development in the USDA rice collection
Jeremy Edwards, Scientist – USDA Agricultural Research Service; Ming-Hsuan Chen,
Scientist – USDA Agriculture Research Services; Aaron Jackson – USDA Agriculture
Research Services; Anna McClung, Plant Breeder – USDA Agriculture Research Services
Uncovering underlying genetics associated with grain quality is important to world food
security. Rice is consumed as a whole grain, therefore cooked rice texture, stickiness,
chewiness, grain dimensions and grain appearance can affect palatability and
marketability. Amylose and protein content play significant roles in determining eating
and cooking quality and affect the translucency of milled kernels. Kernel translucency is
influenced by the presence of chalk, an opaque area in the grain, which occurs when starch
granules are loosely packed in the endosperm. The minicore (MC) panel is a
representative germplasm subset of the USDA Rice Core Collection, specifically designed
to capture maximum diversity in a manageable size and is ideally suited for genome-wide
association (GWA) experiments that have a high phenotyping cost. The publically
available re-sequencing dataset of the 203 MC accessions by next generation sequencing
(NGS) produced ~3.3 million SNPs. The SNPs were used to conduct GWA analysis on the
grain traits, apparent amylose content (AAC), alkali spreading value (ASV), percent grain
chalk (Chk) and percent grain protein (Prot). Major known starch related genes, such as
soluble starch synthase IIa (SSIIa) and Waxy, were identified, as well as 11 novel grain
quality loci, seven novel chalk loci and seven novel protein loci. Further analysis of regions
surrounding significant SNPs with Perl scripts identified several overlapping
chromosomal regions associated with multiple traits. These results will be instrumental
in determining molecular markers useful in marker assisted selection for grain quality
and may provide insights into the biological processes that influence it.
Organ Pipe Cactus - 48
Arno Meiring, MA Yokohama National University
Genetic analysis of multiple elements including iodine and cesium in brown
rice grown in field and controlled conditions
Tatsuo Nakamura, PhD – Yokohama National University
Rice is a staple food for large parts of Asia and Africa, and serves as a model organism for
cereals. Information regarding the accumulation of elements in rice grains is essential to
expanding the understanding of plant physiology and genetics, and is a powerful tool for
developing healthier crops. As such, this study investigates element accumulation in
multiple populations across field and controlled environments. Multi-element analysis
was conducted on brown rice harvested from 69 accessions of the World Rice Core
Collection (WRC) and 50 accessions from the Rice Core Collection of Japanese Landraces
(JRC) maintained by the NIAS Genebank, as well as on 98 backcrossed inbred lines (BIL)
and 54 chromosome segment substitution lines (CSSL) derived from Nipponbare ×
Kasalath, obtained from the Japanese Rice Genome Resource Center. All populations
were cultivated both in the field using standard agronomic practices for the region, as well
as in controlled indoor conditions. Essential nutrients and other elements of interest were
supplied using a modified version of the Kimura-B growth medium. Elemental analysis
was performed using inductively coupled plasma mass spectrometry (ICP-MS) to analyze
grain content for 20 elements, including magnesium, phosphor, potassium, manganese,
iron, zinc, copper, arsenic, cadmium, iodine and cesium. Association and linkage
mapping was performed in the form of a genome-wide association study (GWAS) and
quantitative trait loci (QTL) analysis respectively, revealing potential targets for marker
assisted selection.
Organ Pipe Cactus - 49
Karina Morales, Texas A&M University
Characterizing a rice diversity panel with a 7K SNP chip and flowering time
evaluation
Stephon Warren – Texas A&M University; John Carlos Ignacio – International Rice
Research Institute; Yuxin Shi – Cornell University; Rodante Tabien – Texas A&M
AgriLife Research; Tobias Kretzschmar – International Rice Research Institute; Susan
McCouch – Cornell University; Michael Thomson – Texas A&M University
Rice (Oryza sativa L.) is an essential food crop with demands for increased yield as it
provides the daily caloric intake of over 50% of the world’s growing population. Flowering
is one of the most sensitive stages of rice growth and is highly variable among varieties
and across environments. In Texas, farmers often desire early flowering varieties as these
can avoid peak temperatures of the summer months and give sufficient time for the ratoon
crop to mature before the cold temperatures of winter begin. This experiment took place
at the Texas A&M AgriLife Research Center in Beaumont, TX where 208 rice varieties of
diverse origins were planted in spring 2017 and were grown through the summer of 2017.
Beginning approximately 50 days after planting, notes were collected once a week on
flowering percentage to estimate days to 50% flowering. Each variety was genotyped using
the Illumina 7K rice SNP chip developed at Cornell University. This project aims to
identify genetic loci which contribute to extremely early and late flowering time. Upon
identifying these loci, we will use the CRISPR/Cas9 genome editing system to validate
candidate genes in diverse genetic backgrounds to gain a better understanding of how
each locus may contribute to days to heading in rice. Ultimately, the improved knowledge
on manipulating flowering time genes will lead to more precise tools to provide early
flowering in any genetic background for each target environment.
Organ Pipe Cactus - 49
Marjorie Lundgren, PhD Massachusetts Institute of Technology
C4 anatomy can evolve via a single developmental change
While the vast majority of flowering plants use C3 photosynthesis, some lineages evolved
the C4 pathway to overcome environmental limitations on carbon fixation. In most C4
plants, the carbon assimilation and reduction steps of photosynthesis differentially occur
in leaf mesophyll and bundle sheath tissue types, respectively, while C3 plants complete
both steps simultaneously within the mesophyll. Thus, leaf anatomy is tightly linked to
photosynthetic pathway, with C3 plants requiring large mesophyll volumes for
photosynthesis, and efficient C4 leaves requiring large bundle sheath volumes to
accommodate the necessary photosynthetic organelles, but little mesophyll. Because C4
photosynthesis requires specific leaf anatomy, its evolution is assumed to involve
important modifications to several anatomical traits. Indeed, C4 plants typically achieve
enlarged bundle sheath and reduced mesophyll areas compared to C3 plants, but the
underlying modifications have only been assessed through species comparisons, which
likely capture numerous changes besides those necessary for C4 functionality. The grass
Alloteropsis semialata provides a unique intraspecific continuum of closely related C3,
C3-C4 intermediate, and C4 states, allowing me to determine the minimum anatomical
changes that accompanied the transition between non-C4 and C4 phenotypes, and
distinguishing the anatomical changes that occurred only after C4 emergence. Here, I
show that only an increase in vein density, driven specifically by minor vein development,
distinguishes C4 from non-C4 plants. Furthermore, using a rare C3 x C4 F1 hybrid of A.
semialata, I show that this important minor vein phenotype is genetically determined and
can be inherited via C3 x C4 hybridization. Exploiting this intraspecific diversity, my
results show that a single developmental change is sufficient to produce functional C4 leaf
anatomy, partially explaining the recurrent origins of this complex trait and providing key
information needed for engineering the efficient C4 phenotype into C3 crops < ./p>
Organ Pipe Cactus - 50
Marek Zivcak, Slovak University of Agriculture in Nitra
Use of hyperspectral data to assess leaf traits of diverse wheat genetic
resources in the field
Marek Zivcak – Slovak University of Agriculture in Nitra, Slovakia; Marian Brestic –
Slovak University of Agriculture in Nitra, Slovakia; Lenka Botyanszka – Slovak University
of Agriculture in Nitra, Slovakia; Pavol Hauptvogel – National Agricultural and Food
Centre in Piestany, Slovakia
Hyperspectral analysis has been introduced as an alternative technology to characterize
the different properties of crop canopies, including applications in field phenotyping of
genetic resources. However, the open question is the reliability of hyperspectral indices
in the estimation of leaf properties when applied in a broad spectrum of genotypes
differing in plant and leaf morphology, anatomy and chemical composition of leaves. To
examine this issue, we tested the set of wheat genebank accessions with a broad
phenotypical diversity using the hyperspectral field records as well as the subsequent
analyses of phenotypic and physiological traits, such as leaf area, leaf thickness (measured
as leaf mass per area unit, LMA), leaf nitrogen content, chlorophyll and carotenoid
content, chlorophyll a to b ratio, chlorophyll to carotenoid ratio, SPAD value, etc. We
found relatively high diversity in all observed traits (thick vs. thin leaves, high vs. low
chlorophyll concentration; very small vs. very large leaves), providing good background
for correlation analyses between the hyperspectral parameters and related phenotypic
traits. We found that the parameters proposed in literature for estimation of some traits
are not useful to be used for germplasm with a large or unknown phenotypic variability.
Anyway, we found a few parameters correlating well across the entire collection of wheat
genotypes, which can be regarded as more reliable and universal, useful for the use in
phenotyping in genebank wheat collections or wheat breeding. Our results can be useful
as a background for the next activities in phenotyping for biomass improvements,
nutrient use efficiency or abiotic stress tolerance in wheat and other crops. The study was
supported by the national grants APVV-15-0721 and VEGA-1-0831-17.
Organ Pipe Cactus - 51
Kevin Falk, MSc Iowa State University
Studies of Root System Architecture in Soybean using Computer Vision
Kevin Falk, MSc – Iowa State University
Root system architecture (RSA) studies are tedious, susceptible to introduced variation,
measurements are time consuming and the extracted features may not translate to
meaningful outcome, i.e., increase in yield or other important traits. With the advent of
computer vision, there is a renewed interest in uncovering “the hidden half”, i.e.,
discovering trait correlations within and between genotypes and phenotypes. This study
included 300 diverse soybean accessions from a wide geographical distribution and
deployed 2-D (in controlled conditions) and stereo imaging platforms (field tests),
processing and data analytic tools to deep phenotype for important RSA traits using in-
house imaging software, ARIA. Both 2-D and stereo imaging platforms reveal tremendous
genetic variability for RSA traits for root shape, length, mass, and angle. The stereo
imaging platform developed for this study makes it possible to phenotype hundreds of
genotypes and extract numerous root system traits. In addition, the 2-D platform
developed is non-destructive, adding observations of seedling root growth and
development.
Arizona Rainbow Cactus - 53
Rishi Masalia, University of Georgia
Phenotypic and transcriptomic responses to overlap in water-related
limitation stresses in cultivated sunflower seedlings
Liana Mosley, Undergraduate – University of Georgia; John Burke, Dr. – University of
Georgia
Water limitation is considered by many to be one the most detrimental effects unto crop
yields, and can occur through a variety of abiotic stresses influencing plant water
potential. As such, understanding how crops respond to water limitation is becoming
increasingly important. One approach is to understand how plants respond to variety of
water limitation stresses and identify commonalities in response. Here, we investigate the
extent to which a single genotype of cultivated sunflower exhibits a shared phenotypic
and transcriptomic response across three water limitation stresses: a repeated soil dry
down, and two osmotic challenges, salinity (100mM NaCl) and PEG-6000 (8.25% by
volume). Phenotypically stressed plants had a decrease in total biomass with a shift
towards root allocation and increased water use efficiency consistent with previous
drought literature. Transcriptionally, we identified 1,332 unique differentially expressed
genes (DEGs) across leaf and root tissue 10 days post-treatment, with a majority of DEGs
unique to an individual stress. Across all stresses, 51 genes were shared and expressed in
the same direction relative to control. Of the treatments surveyed, PEG-6000 and salt
cluster both phenotypically and transcriptionally away from dry down, except in the
subset of shared root DEGs where stressed individuals experienced a distinct treatment
response. Moreover, of the shared 51 DEGs 2 root DEGs colocalize with previous genome-
wide association results of sunflower seedlings exposed to PEG-6000 stress at the same
concentration (8.25% by volume). This shared response suggests that efforts aimed at
producing plants that are more resilient to a particular water limitation stress may convey
benefits in other stresses.
Organ Pipe Cactus - 52
Max Feldman, Donald Danforth Plant Science Center
The trait components that constitute whole plant water use efficiency are
defined by unique, environmentally responsive genetic signatures in the
model C4 grass Setaria
Max Feldman – Donald Danforth Plant Science Center; Patrick Ellsworth – Washington
State University; Asaph Cousins – Washington State University; Ivan Baxter – USDA-
ARS
The complex relationship between plant growth and water use is largely determined by
genetic factors that influence both the morphological and biochemical characteristics of
plants. Improving the efficiency by which plants utilize water is an important breeding
objective that can be translated to improve productivity in agriculture while
simultaneously making it a more sustainable endeavor. To assess the genetic basis of
water use efficiency and trait plasticity, we have utilized high-throughput phenotyping
platform and mass spectrometry to quantify plant size, evapotranspiration and stable
isotope composition of an interspecific Setaria italica x Setaria viridis recombinant inbred
line population in both a well-watered and water-limited environment. Our findings
indicate that measurements of plant size and water use in this system are strongly
correlated. We used a linear modeling approach to partition the traits into the predicted
values of plant size given water use and deviations from this relationship at the genotype
level. The resulting traits describing plant size, water use, water use efficiency and
δ13C are all substantially heritable, responsive to soil water potential differentials
and provide a framework to understand the components of plant water use efficiency.
Biparental linkage mapping successfully identified several pleiotropic loci that exhibit
medium-to-large effects on most traits in addition to many smaller effect loci associated
with fewer traits or specific to well-watered or water-limited environments. This study is
the first report characterizing the genetic architecture of water use efficiency in the model
C4 species Setaria and mechanistically links measurement of water use efficiency with
δ13C through several common large effect QTL.
Arizona Rainbow Cactus - 54
Chenyong Miao, PhD - University of Nebraska-Lincoln
Analysis of sorghum time-series phenotype data using nonparametric
regression and machine learning
Chenyong Miao – University of Nebraska-Lincoln; Piyush Pandey – University of
Nebraska-Lincoln; Zhikai Liang – University of Nebraksa-Lincoln; Daniel Carvalho –
University of Nebraksa-Lincoln; Xiaoyang Ye – University of Nebraska-Lincoln; Vincent
Stoerger – University of Nebraska-Lincoln; Yuhang Xu – University of Nebraska-Lincoln;
Yufeng Ge – University of Nebraska-Lincoln; James Schnable – University of Nebraska–
Lincoln
With the rapid development of high throughput phenotyping technology, time-series
phenotype data can be easily obtained in many crop species such as sorghum and maize.
Time-series phenotype data provides more information than traditional manually
measurements in the field. However, the analysis of the time-series phenotype data and
comparisons across different plant accessions also presents some challenges which are
not issues for single time point phenotypic datasets. For example, variation in flowering
time can have pleiotropic effects on many other plant traits, which can confound efforts
to characterize the distinct genetic architectures controlling non-flowering time
phenotypes in the growing season. Here we present data of 347 sorghum accessions from
the sorghum association panel (SAP). Plants were imaged using RGB and hyperspectral
cameras every two days at the UNL Greenhouse innovation center from July 7th to August
31st, which spanned flowering stages for the vast majority of these accessions. This
dataset was used to explore a number of approaches to reduce the confounding effects of
flowering time variation on quantitative genetic analyses. The first approach employed
uses manual scoring of the flowering dates to sort and compare phenotypic measurements
relative to the date of flowering rather than the date of planting. The second approach
employed builds on incorporating nonparametric curve fitting method to increase
measurement accuracy and quantify “rate of change” phenotypes. Finally, the 20,000
sorghum images scored for flowering were used to train a machine learning classifier to
distinguish images of flowering and vegetative stage of sorghum plants, which greatly
decreasing labor costs of using flowering time as the indexed traits in high throughput
phenotyping contexts.
Arizona Rainbow Cactus - 55
Fabiana Moreira, Msc - Purdue University
Improving Efficiency of Soybean Breeding with Phenomic-enable Canopy
Selection
Fabiana Moreira – Purdue University; Anthony Hearst – Purdue University; Keith
Cherkauer – Purdue University; Katy Martin Rainey – Purdue University
The Soybean Breeding program at Purdue University determined that average canopy
coverage (ACC) as measured by UAS (unmanned aerial systems) is highly heritable, with
a high genetic correlation with yield. In this study, we compare selection for ACC to
selection for yield in the early stages of a soybean breeding pipeline. In 2015, we collected
UAS imagery 8 times around 13 to 56 days after planting in progeny rows to determine
ACC for each plot. The best soybean lines were then selected with three parameters: Yield,
ACC and Yield given ACC (Yield|ACC), and the lines were assessed at two locations in
2016. We found that performance of lines in the selection categories to be statistically
equivalent. Additionally, ACC selected 4 of the top 10 ranked lines, and selected generally
earlier-maturing lines. We repeated the progeny row selection in the next cycle in 2016,
with 7 surveys around 13 to 56 days after planting, but in 2017 the yield selection
categories out-performed ACC selection, perhaps because canopy growth was unusually
high in 2016. Rankings were also less favorable, out of the top 10 ranked lines, ACC
selected 1. Even though, ACC did not result in the best performance lines in the second
year of selections, the cost and time involved in harvesting thousands of lines usually leads
breeders to perform visual selection and our results indicate that ACC has a role in
efficient selection of high-yielding soybean lines.
Arizona Rainbow Cactus - 56
James Ta, University of California, Davis
Camera phenotyping and path analysis to reveal indirect effects of genetic
architecture in the shade avoidance syndrome
James Ta – University of California, Davis; Daniel Runcie – University of California,
Davis
Quantitative trait loci (QTL) mapping is an important tool for understanding the
genotype-phenotype relationship and explaining genotype-by-environment interactions.
QTL mapping studies have revealed disease and yield-related QTLs that have been pivotal
in increasing agricultural production. However, many of these studies rely on single-time
point phenotypes to characterize developmental stages. Because these traits do not
capture development throughout time, single-time point analyses might not capture
transient QTLs or QTL effects that change in smaller intervals of time. Consequently,
using high-throughput phenotyping (HTP) – in conjunction with QTL mapping – can
uncover novel QTLs because these systems can fully capture multiple stages of
development. This provides more data to detect QTLs with higher statistical power, and
greater potential for further modeling. For this study, we use a camera phenotyping
system, along with manually-measured traits, to investigate the genetic architecture of
the shade avoidance syndrome (SAS) in A. thaliana. The SAS is a collection of responses
that plants display when shaded by other plants. The genetic control of the SAS at the
hypocotyl stage is well-known; however, the genetic architecture underlying the SAS
beyond the hypocotyl is less understood. Because shading influences plant growth over
time, we can use HTP and modeling to reveal novel SAS QTLs throughput development
and better understand the SAS in an integrated way. We identify several development-
specific SAS QTLs, which potentially involve new SAS genes or gene modules. Using path
analysis, we show that some of the effects of these QTL on later-development traits can
be explained as indirect effects arising from direct effects earlier in development. Our
work provides greater insight into how multiple elements – genetics, environment, and
the relationships between phenotypes – integrate to influence the genotype-phenotype
map.
Sensors and Systems
Arizona Rainbow Cactus - 57
Farzad Hosseinali, Texas A&M University, Biological & Agricultural Engineering
Department
Quantifying Nanoscale Biomechanical Properties of the Plant Cuticular
Waxes
Farzad Hosseinali – Texas A&M University, Biological & Agricultural Engineering
Department; Alex Thomasson – Texas A&M University, Biological & Agricultural
Engineering Department; James Batteas – Texas A&M University, Chemistry
Department; Eric Hequet – Texas Tech University, Plant and Soil Science Department;
Jane Dever – Soil and Crop Science Department; Edward Barnes – Cotton Incorporated
The potential applications of Atomic Force Microscope (AFM) in quantifying the
biomechanical properties of plants tissue and membranes, such as the cuticle of tomato
fruits, have been introduced before. However, previous studies on the application of the
AFM in the surface characterization of cotton fiber were mainly focused on the AFM
capabilities in producing high-resolution topography images of either fiber surface or its
cross–section. In fact, cotton fiber cells are covered with a thin cuticular membrane. The
cuticle is mostly made of lipids, alcohols, and fatty acids (collectively called ‘cotton wax’).
The waxy layer can be 10 to 300 nm thick and imparts hydrophobicity to the fiber surface.
The main objective of this study was to characterize and compare the surface
nanomechanical properties of cotton fibers using various modes of the AFM. Surface
topography and friction images of the fibers were obtained with conventional contact
mode. The nanomechanical property images, such as adhesion and deformation, were
obtained with Bruker’s newly developed high-speed force-volume technique, PeakForce
QNM®. The differences in nanoscale friction, adhesion, and deformation signals can be
attributed to fiber surface hydrophobicity and stiffness, which in turn depend on fatty
acids’ hydrocarbon chain length, film viscosity, and the waxy layer thickness.
Arizona Rainbow Cactus - 58
William Salter, School of Life and Environmental Sciences, Sydney Institute of
Agriculture, The University of Sydney
Tackling the physiological phenotyping bottleneck with low-cost, enhanced-
throughput, do-it-yourself gas exchange and ceptometry
William Salter – The University of Sydney; Matthew Gilbert – University of California,
Davis; Andrew Merchant – The University of Sydney; Thomas Buckley – University of
California, Davis
High throughput phenotyping platforms (HTPPs) are increasingly adopted in plant
breeding research due to developments in sensor technology, unmanned aeronautics and
computing infrastructure. Most of these platforms rely on indirect measurement
techniques therefore some physiological traits may be inaccurately estimated whilst
others cannot be estimated at all. Unfortunately, existing methods of directly measuring
plant physiological traits, such as photosynthetic capacity (Amax), have low throughput
and can be prohibitively expensive, creating a bottleneck in the breeding pipeline. We
have addressed this issue by developing new low-cost enhanced-throughput phenotyping
tools to directly measure physiological traits of wheat (Triticum aestivum). Our eight-
chamber multiplexed gas exchange system, OCTOflux, can directly measure Amax with
5-10 times the throughput of conventional instruments, whilst our handmade
ceptometers, PARbars, allow us to monitor the canopy light environment of many plots
simultaneously and continuously across a diurnal cycle. By custom-building and
optimizing these systems for throughput we have kept costs to a minimum, with
OCTOflux costing roughly half that of commercially available single-chamber gas
exchange systems and PARbars costing approximately 95% less than commercial
ceptometers. We recently used these tools to identify variation in the distribution of Amax
relative to light availability in 160 diverse wheat genotypes grown in the field. In a two-
week measurement campaign we measured Amax in over 1300 leaves with OCTOflux and
phenotyped the diurnal light environment of 418 plots using 68 PARbars. These tools
could be readily modified for use with any plant functional type and also be useful in
validating emerging HTPPs that rely on remotely sensed data to estimate photosynthetic
parameters.
Arizona Rainbow Cactus - 59
Oliver Scholz, Fraunhofer Development Center X-Ray Technology
Phenotyping for Plant Breeding using 3D Sensors and a Generic 3D Leaf
Model
Franz Uhrmann – Fraunhofer Development Center X-Ray Technology; Katharina Pieger
– Fraunhofer Development Center X-Ray Technology; Dominik Penk – University of
Erlangen-Nuernberg; Guenther Greiner, Prof. – University of Erlangen-Nuernberg
We present a setup to objectively assess sugar beet plant traits using multiview
stereoscopy with color cameras with the goal of generating relevant phenotyping
parameters to aid the breeder. The setup was tested in a greenhouse environment as well
as in the field. The assessment is performed using a generic leaf model adapted to the
specific requirements of the sugar beet breeder. The evaluation yields global plant
parameter including plant height, leaf area, leaf count, etc. as well as per-leaf parameters
for each leaf of the plant. The leaf model is based on semantic geometric parameters,
which can directly be interpreted by the breeder.
Arizona Rainbow Cactus - 60
Cory Hirsch, Dr. University of Minnesota
Machine vision phenotyping platform for seedling growth and morphology
Tara Enders – University of Minnesota; Nathan Miller – University of Wisconsin-
Madison; Elizabeth Sampson – University of Minnesota; Sara Tirado – University of
Minnesota; Nathan Springer – University of Minnesota; Edgar Spalding – University of
Wisconsin-Madison
The ability to link genotypes and phenotypes can be used to improve plant productivity
and our understanding of plant biology. Our ability to obtain genomic information
efficiently and accurately has advanced greatly, while phenotyping methods have largely
remained laborious, subjective, and/or expensive. Towards alleviating these barriers, we
have developed a user friendly and affordable platform to acquire highly standardized
RGB images, while relying on minimal equipment and space in a laboratory setting.
Currently, we have developed algorithms to extract numerous growth and morphological
traits including plant height, width, stem diameter, pixel area, and center of mass. The
traits our algorithm extract correlate well with both traditional hand measurements and
measurements using manual image analysis techniques. We are leveraging available
storage, application deployment, and compute resources through the infrastructure at
CyVerse to allow accessibility to almost any researcher. This platform is already being
used by multiple research groups at multiple Institutions and has been optimized for daily
collection of images of multiple plants to easily look at plant development. We have used
this method to monitor growth rate variation among different maize genotypes subjected
to temperature stress and also to measure variation in heterosis for seedling growth in a
panel of inbred and hybrid genotypes.
Arizona Rainbow Cactus - 61
Mitchell Feldmann, UC Davis Strawberry Research Program
Quantitative Methods for Studying Fruit Morphology in Strawberry
Yash Bhartia – UC Davis; Scott Newell – UC Davis; Julia Harshman – UC Davis; Steven
Knapp – UC Davis Strawberry Research Program
Several phenotypic characteristics, including, shape, and external color, are determinants
of both grower- and consumer-centric fruit quality in strawberry (Fragaria × ananassa).
Strong artificial selection for superior shelf-life, increased yield of marketable fruit, and
other commercial production traits has significantly changed fruit morphology and
quality attributes to produce high yielding cultivars with large, ultra-firm fruit. The
genotype-to-phenotype networks underlying these changes have not been investigated in
depth, and genes targeted by selection have not yet been identified in strawberry.
Moreover, it remains unclear what level of phenotypic complexity is necessary and
sufficient to support genomic-based inquiries and discoveries, expand what is known
about modern germplasm, and enhance breeding practices in strawberry. Our current
work explores approaches for and challenges associated with quantifying fruit size, shape,
and color from one-dimensional (e.g. height and width), two-dimensional (e.g. area and
cross-section shape), and three-dimensional (e.g. volume and surface topology)
perspectives. We demonstrate methods for collecting, measuring, and classifying digital
images for 1-D and 2-D analyses and suggest a method for reconstructing fruit models
from multiple images for 3-D analyses. Multivariate and spatial statistics will be used to
determine parameters paramount in identifying and quantifying fruit defects,
differentiating between marketable and non-marketable fruits, and understanding fruit
phenotypes critical for markets that require long shelf-life and sustained fruit quality, as
impacted by harvesting, handling, and shipping.
Arizona Rainbow Cactus - 62
Mahendra Bhandari, Texas A&M University
Assessing Wheat Foliar Disease Severity Using Ground- and Aerial-based
Remote Sensing Systems
Amir Ibrahim – Texas A&M University; Qingwu Xue – Texas A&M University; Haly Neely
– Texas A&M University; Nithya Rajan – Texas A&M University; Jinha Jung – Texas
A&M AgriLife Research; Murilo Maeda – Texas A&M AgriLife Research; Juan Landivar
– Texas A&M AgriLife Research; Bryan Simoneaux – Texas A&M University; Geraldine
Opena – Texas A&M University; Anil Adhikari – Texas A&M University
Remote sensing has been widely used as an indirect approach to study the agronomic and
physiological traits of plants. Plant diseases cause significant yield reductions in wheat
(Triticum aestivum L.). Remote detection and assessment of plant diseases are important
to improve disease phenotyping in breeding programs. This study investigates the
potential use of low-cost Unmanned Aerial System (UAS), equipped with RGB and
multispectral sensors, to quantify leaf rust severity caused by the fungus Puccinia
Triticina in wheat. In addition, Green seeker was used to taking Normalized Difference
Vegetation Index (NDVI) measurements. RGB images were acquired using rotary wing
(DJI Phantom 4 pro) from the field of different wheat genotypes grown at Castroville, TX.
Different vegetation indices (VIs) and image classification approaches were applied to
separate the diseased and healthy canopies. The obtained image dataset was further
processed to generate plot level data. The relationship between VIs and visual field data
on disease severity was examined to find a suitable vegetation index that can evaluate the
leaf rust severity in wheat. Disease severity was highly correlated to excess green index
(r= -0.86), NDVI (r= -0.91, P<0.01). The results show that UAS imaging and automated
data extraction can help to obtain high throughput phenotyping data on disease severity
with higher precision. This tool has a great potential to enable rapid assessment of the
large breeding nurseries by providing high-resolution measurements from small plots
and observation rows.
Arizona Rainbow Cactus - 63
Subodh Bhandari, Cal Poly Pomona
Measurement and Validation of Plant Water and Nitrogen Stresses using
UAV-based Remote Sensing and Machine-Learning Techniques
Amar Raheja, PhD – Cal Poly Pomona; Mohammad Chaichi, PhD – Cal Poly Pomona;
Robert Green, Lecturer – Cal Poly Pomona; Dat Do, Graduate Student – Cal Poly
Pomona; Mehdi Ansari, Graduate Student – Cal Poly Pomona; Frank Pham, Graduate
Student – Cal Poly Pomona; Joseph Wolf, Undergraduate Student – Cal Poly Pomona;
Tristan Sherman, Undergraduate Student – Cal Poly Pomona; Kevin Gonzalez, Graduate
Student – Cal Poly Pomona; Antonio Espinas – Cal Poly Pomona
The presentation talks about the Unmanned Aerial Vehicle (UAV)-based remote sensing
and machine learning techniques to measure water and nitrogen stresses. The main
advantage of UAV-based remote sensing is the immediate availability of high resolution
data. Near infrared (NIR) images obtained using remote sensing techniques help
determine the crop performances and stresses of a large area in a short amount of time
for precision agriculture, which aims to optimize the amount of water, fertilizers, and
pesticides using site-specific management of crops. However, for widespread usages of
these techniques on a routine basis by the end users, the accuracy of remote sensing data
must be validated using the proven ground-based methods. Equally important is the
reduction in the overall cost associated with these techniques. UAVs equipped with
multispectral sensors and digital cameras are flown over lettuce and citrus plots at Cal
Poly Pomona’s Spadra farm. Different rows of lettuce plot is subject to different level of
water and nitrogen treatments. The soil moisture and nitrogen levels were determined
prior to beginning the study. The multispectral images are used in the determination of
normalized differential vegetation index (NDVI) that provides information on the health
of the plant. Machine learning classifiers are developed using the Red-Green-Blue (RGB)
images. Handheld Spectroradiometer, Water Potential Meter, and Chlorophyll Meter are
used for ground-truthing. Correlation between NDVI, chlorophyll content, and water
potential will be shown. The developed machine learning algorithm is able to predict the
plant health reasonably well. Machine learning techniques with sufficient validation have
potential to provide significantly cheaper solutions to plant health assessment using just
the digital cameras.
Arizona Rainbow Cactus - 64
Menglu Wang, Bsc Department of Plant Sciences, University of Saskatchewan
Can Satellite Imagery be Used in Phenotyping?
Menglu Wang – University of Saskatchewan; Hema Duddu – University of Saskatchewan;
Steve Shirtliffe – University of Saskatchewan; Ti Zhang – University of Saskatchewan;
Sally Vail – Agriculture Agri-Food Canada
The application of small unmanned air vehicle (UAV) in phenotyping has become
common because it can capture high resolution imagery of crops in field plots. However
the infrastructure, complexity, and regulatory requirements are impediments to
adoption. High resolution satellite imagery is available as an alternative imagery source,
however the utility of this technology to phenotype crops at the small plot scale has not
been tested. The objective of this study is to compare extracted dataset from satellite and
drone to determine if satellite imagery can be used for field phenotyping of small plots.
An oat trial with different nitrogen treatments (0, 50, 100, and 150 kg ha-1) was utilized
for this study. Geoeye-1 satellite multispectral imagery (Blue, Green, Red, and Near
Infrared) with a spatial resolution of 50cm and UAV imagery captured with Micasense
multispectral Rededge camera on the same date with a ground resolution of 2cm with
similar spectral bands. Vegetative indices, including normalized difference vegetation
index (NDVI) and optimized soil adjusted vegetation index (OSAVI) were calculated for
individual and groups of field plots. Vegetation indices from satellite and UAV imagery
for grouped plots and within the groups were strongly correlated. Results from other trials
with different traits will be presented in poster. Therefore, satellite imagery has potential
for phenotyping at the small plot scale for some traits. However, atmospheric effects and
the temporal resolution will still remain issues for satellite imagery.
Arizona Rainbow Cactus - 65
Caitlin Moore, University of Illinois
Linking solar induced fluorescence with genetic variability in productivity of
biomass sorghum
Katherine Meacham – University of Illinois; Guofang Miao – University of Illinois; Taylor
Pederson – University of Illinois; Evan Dracup – University of Illinois; Xi Yang –
University of Virginia; Kaiyu Guan – University of Illinois; Carl Bernacchi – University of
Illinois
The measurement of solar induced fluorescence (SIF) of chlorophyll has emerged as a
useful tool for monitoring plant photosynthesis. Application of SIF at the regional scale
from satellite remote sensing has delivered promising results, with strong links found
between SIF and gross primary productivity. Our ability to use SIF as a tool to monitor
photosynthesis has the potential to enhance agricultural advancement by facilitating the
identification of better performing individuals at a faster rate. However, this kind of high-
throughput phenotyping is usually achieved at the plot and/or leaf scale and there
remains an understanding gap as to what extent SIF can capture plot and leaf scale
variation in photosynthetic activity. We tested the ability of SIF to capture photosynthetic
variability at the plot scale in a C4 biomass sorghum field and at the leaf scale in a C3
tobacco experiment grown under field conditions in Central Illinois, USA. To do this, we
built a portable SIF system to collect high-resolution measurements of SIF and coupled
these with measurements of leaf-level gas exchange and photosynthetic performance
indicators (i.e. Vcmax & Jmax), in situ chlorophyll fluorescence, leaf chlorophyll content,
spectral reflectance indices and leaf area. This presentation will discuss not only the
results from our field experiments, but also some of the lessons we have learned along the
way towards developing SIF into a high-throughput phenotyping tool for use at the leaf
and plot scales.
Pancake Prickly Pear Cactus - 66
Jaderson Armanhi, University of Campinas
A real-time, non-invasive, low-cost monitoring system for plant phenotyping
under stress
Rafael de Souza – University of Campinas; Paulo Arruda – University of Campinas
Phenotypic data are essential to understanding plant responses to environmental
changes. Conventional instruments to assess plant physiological status are often invasive
or destructive, such as pressure chambers and tensiometers, or designed to provide a
single data point, such as the infrared gas analyzer (IRGA) and the porometer. Although
these methods are reliable, they do not provide continuous monitoring of plant response
to environmental stresses, which might result in losses of relevant information regarding
the true physiological status and plant adaptation mechanisms. Real-time phenotyping
technologies are usually costly, and most platforms are restricted to phenotyping
facilities. Therefore, the development of low-cost phenotyping options is exceptionally
convenient for small-scale studies and experimental setups under growth chamber and
greenhouse conditions. Here we propose a simple and real-time monitoring system for
the remote study of plant physiology using low-cost and easy-to-handle electronic
components. Our system provides the constant monitor of leaf temperature, vapor
pressure deficit (VPD), soil moisture, water loss, as well as the air temperature, relative
humidity and light intensity. An integrated RGB camera was used to record plant
response over time and a modified camera was used to capture near-infrared images for
NDVI measurements. All sensors and cameras are connected to a microcontroller
Raspberry Pi that receives and processes signals and images through custom and
automated scripts. Real-time data are sent to an online server that plots graphs and
creates time-lapse movies on a webpage. Sensors and methods are currently being
validated in experiments designed to evaluate drought stress response in maize. By
providing temporal high-resolution data and imaging, our small-scale system has the
potential to bring valuable information on plant phenotyping in a low-cost manner.
Pancake Prickly Pear Cactus - 67
Nicholas Kaczmar, Cornell University
Application of unmanned aerial vehicles for high-throughput phenotyping of
canopy traits in maize
Ethan Stewart – Cornell University; Michael Gore – Cornell University
The advent of affordable unmanned aerial vehicle (UAV) technologies and relaxation of
federal regulations make UAVs appealing tools for collecting plant phenotypic data in
field environments. Routine phenotypic measures such as plant height are important for
genetic studies and breeding programs, but are time and labor intensive to collect.
Additionally, vegetation indices like the normalized difference vegetation index (NDVI)
are typically measured by plane or satellite platforms, thus not offering the resolution
needed to assess individual field trial plots. UAVs offer the potential to gather large field
data sets at high spatial and temporal resolution. The Genomes to Fields (G2F) Initiative
is a publicly initiated and led research initiative supporting translation of maize genomic
information for the benefit of growers, consumers and society. UAVs fitted with RGB and
multispectral cameras were flown at low elevation over two G2F reps of approximately
500 maize hybrids. These flights were conducted at 6 time points per growing season at
the Cornell Musgrave Research Farm in Aurora, NY from 2015-17. Images were stitched
together and geo-referenced using ground control points of known positions to give geo-
referenced orthomosaic images of the field. Digital elevation models and 3D point clouds
were produced from the RGB stitched images, allowing mean plant height to be calculated
for each plot. In addition, plot level NDVI was calculated from the multispectral images.
Such higher resolution data sets will allow the underlying genetic basis of dynamic
phenotypes to be more extensively studied.
Pancake Prickly Pear Cactus - 68
Kaitlyn Read, University of New Mexico
Tissue specific electrical impedance as a potential screening tool
Patrick Hudson – University of New Mexico; Philip Miller – Sandia National
Laboratories; David Hanson – University of New Mexico
Electrical Impedance Spectroscopy (EIS) is a commonly used noninvasive method to
predict root dimensions, tissue damages, and other physiological parameters. These
methods typically rely on measuring through an electrically variable medium (ie soil,
hydroponic fluid, and epidermal layers), or destructively removing part of the plant. Here
we demonstrate the utility of microneedles to apply EIS methods to specific organs and
tissues in planta. Microneedles were placed on both the adaxial and abaxial surfaces of a
sorghum (Sorghum bicolor) leaf midrib, to measure water storage and water transport
tissues, respectively. An 18-gauge needle was placed 1 cm below the leaf-stalk junction to
function as the signal receiver for both microneedle placements. A handheld LCR meter
supplied a voltage of 0.6V AC, and measured impedance and phase angle at four different
frequencies. Microneedle impedance values were compared to planar metal transducers
as a control, which didn’t penetrate the plant tissue, and impedance values across all
frequencies tested were significantly lower with the microneedle devices. After in planta
EIS measurements were concluded, a fully expanded leaf was removed. Water storage
and water transport tissues were dissected, and EIS measurements were repeated in the
isolated tissues. Impedance was significantly lower in water transport tissue compared to
water storage tissue, both in planta and in isolation. One week after in planta
measurements, leaves showed no adverse response to microneedle applications, other
than superficial callose deposition at the injection site. Our results show that microneedle
EIS can distinguish specific tissues in a non-destructive fashion, and offer a novel
opportunity for high resolution, real-time plant monitoring.
Pancake Prickly Pear Cactus - 69
Grégoire Hummel, PhD, CEO Phenospex B.V.
PlantEye F500: combine 3D and multispectral information in one sensor
PlantEye is a high-resolution 3D laser scanner that computes a robust and validated set
of morphological plant parameters fully automatically. A core feature of PlantEye is that
it can be operated in full sunlight without any restrictions - crucial for plant phenotyping
under field conditions or if you follow a “sensor-to-plant-concept”. Phenospex has now
developed a new sensor generation, which combines the actual features of PlantEye on
the fly with a 4-channel multispectral camera in the range between 400 – 900nm. This
unique hardware-based sensor fusion concept allows us to deliver spectral information
for each data point of the plant in X, Y, Z-direction and we can compute parameters like
NDVI, color index and many other vegetation indices. This new sensor generation opens
a wide range of new possibilities in plant phenotyping and increases its efficiency.
Pancake Prickly Pear Cactus - 70
Miki Fujita, PhD - RIKEN CSRS
Evaluation of Plant Environmental Stress Response using “RIPPS”, an
Automated Phenotyping System
Miki Fujita – RIKEN; Kaoru Urano – RIKEN; Takanari Tanabata – Kazusa Inst.; Saya
Kikuchi – RIKEN; Kazuo Shinozaki – RIKEN
High-throughput and accurate measurements of plant traits facilitate the understanding
of gene function. Especially, with recent advances in quantitative genomics such as QTL
or GWAS, there is a growing need for precise quantification of multiple traits in plants.
However, in the case of environmental stress responses such as drought, it is difficult to
quantify the adaptive responses because multiple environmental factors are intricately
involved in the phenotype. Therefore, precise control of growth conditions is of great
importance to evaluate plant responses to environmental stresses. Recently we have
developed an automatic phenotyping system that evaluate plant growth responses to a
wide spectrum of environmental conditions. The system named RIPPS (RIKEN Plant
Phenotyping System) controls individual soil moisture in continuously rotating 120 pots
by a combination of automatic weighing and watering systems that enable the precise
control of soil water condition is necessary for quantifying the adaptive responses to
environmental stresses such as limited water conditions. RIPPS also take image of top
and side view of the plants every two hours. In this presentation, we’ll demonstrate the
utility of the RIPPS in evaluating drought or salinity tolerance and water use efficiency.
Pancake Prickly Pear Cactus - 71
Yang Yang, Purdue University
The Expanding High-throughput Phenotyping Capabilities at Purdue
University
Mitchell Tuinstra – Purdue University; Erin Robinson – Purdue University; Chris
Hoagland – Purdue University; Jason Adams – Purdue University
To conduct high-throughput plant phenotyping, we need to establish systems capable of
processing plants of various stature in real-time, acquiring non-destructive phenotypic
data, and providing accurately adjusted and variable environmental conditions.
This poster introduces Purdue University’s high-throughput phenotyping platforms with
unique features both in the field and in the controlled-environment facilities. Purdue’s
Indiana Corn and Soybean Innovation Center, a 25,500-square-foot field phenotyping
facility, is designed for research and development of remote sensing platforms such as
UAV-based imaging systems and the PhenoRover, a ground-based mobile sensing
system. Researchers in the facility also use phenotyping equipments such as root and leaf
scanners, root washing stations, seed counters and color sorters, 3D printers and
scanners, ovens and grinders, as well as an automated threshing and shelling line for plant
and seed processing.
The controlled-environment phenotyping facility (CEPF) has the unique feature of
continuously rotating 256 individual plants of up to 3-meter tall in a growth chamber of
which the key environmental parameters are precisely controlled. The autonomous
fertigation system conducts weight-based dosing of water and fertilizer, thereby providing
the capability of establishing different watering and nutrient management regimes. The
RGB and hyperspectral imaging systems provide the capability to monitor plant size and
growth rate with high temporal and spatial resolutions in different plant developmental
stages.
Overall, the phenotyping platforms make it possible to perform reproducible, large-scale
experiments that would not be possible by hand. The synergy of the CEPF and the in-field
phenotyping facility at Purdue University creates a unique data pipeline that enables
better understanding in basic plant physiological processes and empowers researches for
crop yield improvement under various environmental conditions. This synergy also offers
a unique opportunity for evaluating and improving existing phenotyping systems, as well
as developing new technologies.
Pancake Prickly Pear Cactus - 72
Daniel Runcie, PhD - University of California Davis
A Bayesian approach to quantitative genetics for high-dimensional traits
Statistical models for Genome-Wide Association Studies, QTL analysis, and Genomic
Prediction, are the foundation of modern quantitative genetics and crop improvement.
Driven by the explosion of whole-genome genotype data, recent improvements to these
models allow for analyses of millions of markers at a time. However, similar advances for
modeling large phenotype datasets is lacking. New phenotyping technologies collect
thousands of observations on each individual plant or line – changes in morphology
through time, molecular phenotypes such as gene expression or metabolite levels, or
performance measures across multiple environments. Jointly modeling these high-
dimensional traits can provide insight into developmental and physiological mechanisms
that link genotype and phenotype. We propose a robust and efficient method for modeling
the genotype-phenotype relationship of high-dimensional traits. The key idea underlying
our model is that groups of traits will be highly correlated due to genetic and
developmental pleiotropy. We leverage these correlated modules to prioritize the most
important signals in big data. We will demonstrate how our method provides powerful
and interpretable estimates of genetic architecture using two high-dimensional datasets:
a time-series analysis of growth curves, and a dataset of genome-wide gene expression.
Pancake Prickly Pear Cactus- 73
Jian Jin, PhD - Purdue University
Purdue's New Automatic Phenotyping Greenhouse with Micro-climates
Removed
Purdue University deployed a new fully automatic phenotyping greenhouse in May 2017.
This facility is featured for (1) Continuous scanning of each crop plant for up to 20
times/day; (2) Clearly removing the micro-climates impact (the variance of
environmental conditions caused by distribution of lighting, temperature, airflow and so
on across the greenhouse space); (3) Advanced hyperspectral imaging system and data
modeling for plant physiological features predictions. Dr. Jin will also share his view of
next generation plant phenotyping in the next 10 years.
Pancake Prickly Pear Cactus -74
Sierra Young, Iowa State University
Design and Evaluation of a Field-Based High-Throughput Phenotyping
Robot for Energy Sorghum
This article describes the design and field evaluation of a low-cost, high-throughput
phenotyping robot for energy sorghum. High-throughput phenotyping approaches have
been used in isolated growth chambers or greenhouses, but there is a growing need for
field-based, precision agriculture techniques to measure large quantities of plants at high
spatial and temporal resolutions throughout a growing season. A low-cost, tracked mobile
robot was developed to collect phenotypic data for individual plants and tested on two
separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging
techniques determined plant height, and a depth sensor measured stem width near the
base of the plant. A data capture rate of one acre, bi-weekly, was demonstrated for
platform robustness consistent for various environmental conditions and crop yield
modeling needs, and formative human-robot interaction observations were made during
the field trials to address usability. This work is of interest to researchers and practitioners
advancing the field of plant breeding because it demonstrates a new phenotyping
platform that can measure individual plant architecture traits accurately (absolute
measurement error at 15% for plant height and 13% for stem width) over large areas at a
sub-daily frequency.
Pancake Prickly Pear Cactus - 75
James Bunce, PP Systems
High Throughput Photosynthesis Characterization of C3 Plants
Dr. James Bunce, Ph.D. in Plant Physiology – PP Systems; Andrew Lintz, B.S. in
Mechanical Engineering – PP Systems
Single point measurements of leaf gas exchange provide basically only the parameters net
photosynthesis, stomatal conductance, and instantaneous leaf water use efficiency. From
analysis of assimilation rate vs. internal CO2 concentrations (A vs. Ci) curves, four or five
additional leaf parameters are obtained for plants with C3 carbon metabolism, which
allow estimation of photosynthesis over a range of conditions. However, determining A
vs. Ci curves conventionally requires at least 20 minutes per leaf, compared with about 2
minutes for single point measurements, which greatly limits through-put. PP Systems
has developed a method of linearly ramping CO2 rapidly in their CIRAS-3 Portable
Photosynthesis System, which provides a complete A vs. Ci curve in 5 minutes per leaf.
Two initial steps are required: storing the changes in analyzer sensitivity with background
CO2, and collecting data from ramping of CO2 with an empty chamber. These two steps
need only be done once per day. The 5 minute total measurement period per leaf includes
a 2 minute initial equilibration period followed by 3 minutes of ramping up of CO2
concentration until the rate of change of A with CO2 becomes small, i.e. until CO2
becomes nearly saturating to A. With the CIRAS-3 system post-processing of the gas
exchange data is very simple: the apparent “A” of the empty chamber is subtracted from
the “A” value obtained with a leaf in the chamber at each time point of the CO2 ramping
period. This provides the actual A value at each time point, and the Ci is obtained from
this actual A, stomatal conductance, and external CO2 as in the conventional calculation
of Ci. Because of the rapid change in CO2, we have seen no significant change in stomatal
conductance during the CO2 ramps < ./p>
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