Machine Learning-Based Live Weight Estimation for Hanwoo Cow

Changgwon Dang, Taejeong Choi, Seungsoo Lee, Soohyun Lee, Mahboob Alam, Mina Park, Seungkyu Han, Jaegu Lee () and Duytang Hoang ()
Additional contact information
Changgwon Dang: National Institute of Animal Science, RDA, Cheonan 31000, Chungcheongnam-do, Korea
Taejeong Choi: National Institute of Animal Science, RDA, Cheonan 31000, Chungcheongnam-do, Korea
Seungsoo Lee: National Institute of Animal Science, RDA, Cheonan 31000, Chungcheongnam-do, Korea
Soohyun Lee: National Institute of Animal Science, RDA, Cheonan 31000, Chungcheongnam-do, Korea
Mahboob Alam: National Institute of Animal Science, RDA, Cheonan 31000, Chungcheongnam-do, Korea
Mina Park: National Institute of Animal Science, RDA, Cheonan 31000, Chungcheongnam-do, Korea
Seungkyu Han: ZOOTOS Co., Ltd., R&D Center, Anyang 14118, Gyeonggi-do, Korea
Jaegu Lee: National Institute of Animal Science, RDA, Cheonan 31000, Chungcheongnam-do, Korea
Duytang Hoang: ZOOTOS Co., Ltd., R&D Center, Anyang 14118, Gyeonggi-do, Korea

Sustainability, 2022, vol. 14, issue 19, 1-12

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.

Keywords: Hanwoo cow; live weight estimation; machine learning; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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