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Machine Learning-Based Live Weight Estimation for Hanwoo Cow

Authors :
Changgwon Dang
Taejeong Choi
Seungsoo Lee
Soohyun Lee
Mahboob Alam
Mina Park
Seungkyu Han
Jaegu Lee
Duytang Hoang
Source :
Sustainability; Volume 14; Issue 19; Pages: 12661
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Live weight monitoring is an important step in Hanwoo (Korean cow) livestock farming. Direct and indirect methods are two available approaches for measuring live weight of cows in husbandry. Recently, thanks to the advances of sensor technology, data processing, and Machine Learning algorithms, the indirect weight measurement has been become more popular. This study was conducted to explore and evaluate the feasibility of machine learning algorithms in estimating the body live weight of Hanwoo cow using ten body measurements as input features. Various supervised Machine Learning algorithms, including Multilayer Perceptron, k-Nearest Neighbor, Light Gradient Boosting Machine, TabNet, and FT-Transformer, are employed to develop the models that estimate the body live weight using body measurement data. Data analysis is exploited to explore the correlation between the body size measurements (the features) and the weights (target values that need to be estimated) of cows. Data analysis results show that ten body measurements have a high correlation with the body live weight. High performance of all applied Machine Learning models was obtained. It can be concluded that estimating the body live weight of Hanwoo cow is feasible by utilizing Machine Learning algorithms. Among all of the tested algorithms, LightGBM regression demonstrates not only the best model in terms of performance, model complexity and development time.

Details

ISSN :
20711050
Volume :
14
Database :
OpenAIRE
Journal :
Sustainability
Accession number :
edsair.doi.dedup.....220915c6720ac7d9fcfbd19f0dc1be78
Full Text :
https://doi.org/10.3390/su141912661