AI Based Digital Twin Model for Cattle Caring

In this paper, we develop innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work is built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle based on Deep Learning (DL) is generated using the sensor...

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Published in:Sensors (Basel, Switzerland) Switzerland), 2022-09, Vol.22 (19), p.7118
Main Authors: Han, Xue, Lin, Zihuai, Clark, Cameron, Vucetic, Branka, Lomax, Sabrina
Format: Article
Language:English
Subjects:
Agriculture
Algorithms
Analysis
Artificial intelligence
Breeding of animals
Cattle
Dairy cattle
Data acquisition
Data mining
Data processing
Datasets
Deep learning
digital twin
Digital twins
Farm management
Farms
LSTM model
Machine learning
Manufacturing
Model accuracy
Neural networks
Power plants
Product design
Production planning
Remote monitoring
Sensors
Time series
Twins
Wind power
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cited_by cdi_FETCH-LOGICAL-c485t-a2d2531bd937f74ba06d103bbd6ab538d7883fc62acd7a2366f2304bc6092b9a3
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container_end_page
container_issue 19
container_start_page 7118
container_title Sensors (Basel, Switzerland)
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creator Han, Xue
Lin, Zihuai
Clark, Cameron
Vucetic, Branka
Lomax, Sabrina
description In this paper, we develop innovative digital twins of cattle status that are powered by artificial intelligence (AI). The work is built on a farm IoT system that remotely monitors and tracks the state of cattle. A digital twin model of cattle based on Deep Learning (DL) is generated using the sensor data acquired from the farm IoT system. The physiological cycle of cattle can be monitored in real time, and the state of the next physiological cycle of cattle can be anticipated using this model. The basis of this work is the vast amount of data that is required to validate the legitimacy of the digital twins model. In terms of behavioural state, this digital twin model has high accuracy, and the loss error of training reach about 0.580 and the loss error of predicting the next behaviour state of cattle is about 5.197 after optimization. The digital twins model developed in this work can be used to forecast the cattle’s future time budget.
doi_str_mv 10.3390/s22197118
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subjects Agriculture
Algorithms
Analysis
Artificial intelligence
Breeding of animals
Cattle
Dairy cattle
Data acquisition
Data mining
Data processing
Datasets
Deep learning
digital twin
Digital twins
Farm management
Farms
LSTM model
Machine learning
Manufacturing
Model accuracy
Neural networks
Power plants
Product design
Production planning
Remote monitoring
Sensors
Time series
Twins
Wind power
title AI Based Digital Twin Model for Cattle Caring
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