iot and the transformation of aquaculture · iot and the transformation of aquaculture 1 david...

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© Yokogawa Australia

Australian Prawn Farmers Association Symposium 2018

IoT and the Transformation

of Aquaculture

1

David Walker

Chief Engineer

Yokogawa Australia

Aug 2018



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



◼ 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



◼ 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



◼ 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



◼ 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



◼ 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



◼ 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.



◼ 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



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



Edge Computing

◼ Networked Sensors

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◼ Visual Processing

◼ Audio Processing



Analysers

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From one transmitter per measurement…

RTU/Data Concentrator

Plant information system

pH DO Tx Transmitters

Probes



Analysers

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…to one transmitter for all measurements…

RTU/Data Concentrator


pH DO Tx



Analysers

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…to network-enabled probes

Wireless Network

pH DOTx




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



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



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



Cloud Computing

◼ Data Lakes

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◆ Massive data storage

◆ Across multiple locations

◆ From different users and devices



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



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



The Supply Chain

◼ Hatchery

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◆ Variables➢ Season

➢ Temperature profile

◆ Processes➢ Spawning

➢ Supply of stock

➢ Transportation Schedule

SCHEDULE

Spawnersupply

HatcheryMaturation Juvenile

PrawnsTransport



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



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



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



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



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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iot and the transformation of aquaculture · iot and the transformation of aquaculture 1 david...

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