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© Yokogawa Australia
Australian Prawn Farmers Association Symposium 2018
IoT and the Transformation
of Aquaculture
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David Walker
Chief Engineer
Yokogawa Australia
Aug 2018
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© Yokogawa Australia
One Bad Prawn
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◆ A person eats a prawn in a sushi restaurant in Tokyo, and it make her sick
◆ The sushi restaurant contacts the supplier who buys prawns from you
◼ Scenario
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◼ Tracing the batch
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◆ You type in the name of the sushi bar, and the date of the reported incident, into the web browser connected to your cloud-based management system
◆ The system tells you the batch number of the prawns shipped from your processing works
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◼ Batch reporting
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◆ With a click you are given a report with the following information:
➢ Container ID that the batch was shipped in, including date and time of dispatch, loading on ship, offloading at destination
➢ Batch ID at the processing works
➢ Pond number and date of entry and exit
➢ Hatchery batch ID
Prawn batch report
Date 14/08/2018 1:45:23 PM
Destination trace Awasaka Sushi, Ueno, 23418, Japan
ID Status ID Status ID Status
Container ID 23654-123 OK
Batch ID AS456 OK AS457 OK AS458 OK
Pond #34 OK #46 OK #46 OK
Hatchery ID DW78-01 OK DW78-01 OK DW78-01 OK
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◼ Batch Analysis
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◆ The temperature of the container is measured and transmitted to the cloud. You check the temperature trend and it checks out OK.
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Container ID: 23654-123
Temp
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◼ Batch Analysis
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◆ You check the processing works for anomalies reported during the batch. No issues were reported.
◆ The pond information does not indicate an issue, but pond discolouration & high turbidity was reported during the growing period.
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Pond #43 - Turbidity 2018-08
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◼ Batch Analysis
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◆ You perform an analysis of all parameters of the pond at the time of the discolouration.
◆ Parameters include pH, DO, temperature and nitrogen (daily lab analysis).
◆ While each individual parameter is within limits, the combined values indicate a problem which could indicate an infection.
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Pond #43 2018-08
pH DO Temperature Turbidity
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◼ Batch Tracking
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◆ A report tells you which batch IDs contained prawns from that pond at that time.
◆ The batch IDs can be tracked to container IDs and on to shops and restaurants selling prawns.
◆ An email is automatically sent to these shops and restaurants within minutes of being notified advising them of potential problems with prawns received around a particular date.
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◼ Batch Management
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◆ No further incidents are reported
◆ A detailed analysis of the pond at the time of the infection provides a chemical signature of that infection which can be used for detecting similar problems in the future, as they happen
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Pond #43 2018-08
pH DO Temperature Turbidity
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1. Technology Overview
2. Edge Computing
3. Cloud Computing
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Technology Overview
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IT
OT
IOT
IIOT
Big Data Cloud
FogEdgeXaaS
Virtualisation
Mirror Plant
Digital Twin
Digital Mine
PaaS
SaaSAI
Industry 4.0
Digital Transformation
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Internet of Things
Intelligent low cost
sensors and devices,
internet enabled and
plugged into the cloud
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Digital Transformation
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“The use of
digital technology
to transform
all aspects
of the business,
from production
to management”
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Edge Computing
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Edge Computing
◼ Networked Sensors
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◼ Visual Processing
◼ Audio Processing
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Analysers
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From one transmitter per measurement…
RTU/Data Concentrator
Plant information system
pH DO Tx Transmitters
Probes
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Analysers
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…to one transmitter for all measurements…
RTU/Data Concentrator
Plant information system
pH DO Tx
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Analysers
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…to network-enabled probes
Wireless Network
pH DOTx
Plant information system
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Visual Processing
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◼ Pond colour indicates pond health
◼ CCTV can process images and identify changes in colour, translucence, turbidity
◼ This can be fed back to the information system to warn the operator
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Audio Processing
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◼ Prawns have particular frequency signatures
◼ These indicate hunger, sickness and other factors
◼ An edge computer can extract the frequencies from an audio signal and match these against a library of signatures
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Cloud Computing
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Cloud Computing
◼ All computer infrastructure on the internet
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◼ Pay for what you use
◼ Unlimited storage and processing capability
◼ Access to high-powered analytics
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Cloud Computing
◼ Data Lakes
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◆ Massive data storage
◆ Across multiple locations
◆ From different users and devices
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Cloud Computing
◼ Data Analytics
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◆ Converts data to information
◆ …and information to knowledge
◆ Analyses large amounts of data and data
profiles
◆ Correlates different types of data and
information
◼ Platform providers
◆ Microsoft Azure
◆ Google Analytics
◆ Amazon AWS
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Cloud Computing
◼ Analysis and Correlation
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◆ Analyse data profiles (pH, DO, Temperature, pond colour)
◆ Correlate with hatchery and shipping schedules
◆ Build a complete hatchery to consumer model
SCHEDULE
Spawnersupply
Hatchery
FeedstockChemicals
MaturationGrow out
ponds
PrawnsJuvenile
PrawnsProcessing
PROCESSING
MATERIAL
FINISHED
GOODS
PACKAGING
MATERIAL
SHIPPING
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The Supply Chain
◼ Hatchery
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◆ Variables➢ Season
➢ Temperature profile
◆ Processes➢ Spawning
➢ Supply of stock
➢ Transportation Schedule
SCHEDULE
Spawnersupply
HatcheryMaturation Juvenile
PrawnsTransport
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The Supply Chain
◼ Grow-out Ponds
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◆ Variables➢ Season
➢ Temperature profile
➢ pH Profile
➢ DO Profile
➢ Turbidity
➢ Nitrogen
➢ Colour
◆ Processes➢ Pond Biology
➢ Feed
SCHEDULE
Prawn supply
Grow-outPonds
Maturation Prawns Transport
DOpHTemperature
Pond Biology
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The Supply Chain
◼ Processing and Shipment
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◆ Variables➢ Storage temperature
➢ Transport temperature
◆ Processes➢ Time to process
➢ Warehousing and distribution
➢ Port and shipment handling
SCHEDULE
Processing
PROCESSING
MATERIAL
FINISHED
GOODS
PACKAGING
MATERIAL
SHIPPING
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The Supply Chain
◼ Interdependence through the supply chain
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◆ Correlation of different types of information
◆ Example, how to correlate:
➢ Pond pH profile
➢ Shipping schedule
➢ Prawn quality to the consumer
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The Value Proposition
◼ What value can this bring to the industry?
◆ Supply Chain Modelling – optimise the process for production and quality
◆ Tracking and traceability – locate and manage problems quickly
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Supply Chain Model
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SCHEDULE
Spawnersupply
Hatchery
FeedstockChemicals
MaturationGrow out
ponds
PrawnsJuvenile
PrawnsProcessing
PROCESSING
MATERIAL
FINISHED
GOODS
PACKAGING
MATERIAL
SHIPPING
◆ Build Profiles of parameters
◆ Attach to supply schedules
◆ Correlate profiles and schedules
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COMMUNICATION & TRACEABILITY
DEALER PRODUCER CONSUMER SALES
LOGISTICS
PROBLEM CLAIM
STOCK
ARRIVALRAW
MATERIAL
RAWMATERIAL
PROCESS 1 PROCESS 2 INTERMEDIATE
PRODUCT
INTERMEDIATE
PRODUCTPACKING
PACKAGING
MATERIAL
FINISHED
GOODS
PACKAGING
MATERIAL
SHIPPING
Down stream (Trace forward)Up steam (Trace back)
Investigation (Traceability)
PROBLEM
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Conclusion
◼ New technology is transforming aquaculture
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◼ This includes IoT devices, edge computing and cloud-based analytics
◼ Supply chain modelling from hatchery to consumer enables prediction and traceability along the entire chain
◼ The result is optimised production and quality
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