b04 - fire blight - deteccion con multiespectral
TRANSCRIPT
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Towards Automated Detection of
Stress in Tree Fruit Production
J. Park, H. Ngugi, M. Glenn, J.Kim & B. Lehman
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The CIA monitors world-wide,
agricultural production with
satellite-based, remote sensing.
During the Cold War, the U.S.
used this information in the sale
of wheat to Russia
World food production ismonitored to anticipate
governmental instability as well
as markets.
From a global scale to a farm
scale, this technology can be
used to improve grower
productivity.
http://images.google.com/hosted/life/f?q=source:life+apollo+11&prev=/images%3Fq%3Dsource:life%2Bapollo%2B11%26hl%3Den&imgurl=e0ce8a55b1600304http://upload.wikimedia.org/wikipedia/commons/7/7c/CIA_seal.jpg -
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Potential applications of monitoring
technology in tree fruit production
Detection of tree stress
Moisture stress (drought or excess water) Nutrient stress
Disease and insect stress
Estimation of expected yield Any other use?
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Sensor technology for use in
tree fruit production
All sensor-based systems rely on reflected light from
a portion of the electromagnetic spectrum (EMS)
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Reflection(%)
Wavelength (nm)
well watered stressed
Changes in chlorophyll activity
Reflection spectrum of apple leaves
Reduced water content
Visible light Near Infra-red radiation
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Types of sensors being evaluated
in the CASC project
Thermalcameras
NDVI sensors
Hyperspectralcameras
Color cameras
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Detecting fire blight in orchards
Bacterial disease caused by Erwinia amylovor
Often leads to death in young trees
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Factors determining successful
fire blight management
Once infection occurs, successful
management depends on:
Early detection
Application of appropriate control measures
such as cutting out infected shoots
Continued monitoring
All the factors point to the need for
regular scouting!
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Options for
scouting orchards
for fire blight
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Current CASC Project Research
Identification and evaluation of suitable
sensors for automated detection
Preliminary detection experiments Can we detect fire blight with sensors?
How early can we detect lesions?
Development of detection algorithms
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Potential rapid detection systems
for fire blight
Biological-based detection systems
Molecular-based techniques
Can be quite rapid Main challenge is sampling (very large numbers
of samples)
How many shoots (all a potential infection sites)
Destructive sampling
Would be very labor-intensive with current technology
Currently restricted to confirming pathogen identity
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Potential rapid detection systems
for fire blight cont.
Sensor-based detection systems
Rely on sensors to detect plant response to infection
No destructive sampling or sample preparation
Can be as rapid as real-time
Can cover a large area over a short time
Main challenge: the right sensors and developingthe detection algorithms
This is the approach followed in the CASC project
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Sensors evaluated for blight detection
700 nm
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700 nm
Target for early detection:
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Detection of fire blight with
hyperspectral sensor
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Sensors mounted on the APM
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What we hope to accomplish
Detection of diseased shoots within 7
days after infection for fire blight
No more than 1-3 leaves have visible
symptoms for virulent strains
Over 85% accuracy rate
Detection of other types of stress
Develop a database that to help identify
causes of tree stress
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Acknowledgments