A Korean Cattle Weight Prediction Approach Using 3D Segmentation-Based Feature Extraction and Regression Machine Learning from Incomplete 3D Shapes Acquired from Real Farm Environments

Chang Gwon Dang, Seung Soo Lee, Mahboob Alam, Sang Min Lee, Mi Na Park, Ha-Seung Seong, Min Ki Baek, Pham Van Thuan, Jae Gu Lee () and Seungkyu Han ()
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Chang Gwon Dang: National Institute of Animal Science, Rural Development Admission, Cheonan 31000, Republic of Korea
Seung Soo Lee: National Institute of Animal Science, Rural Development Admission, Cheonan 31000, Republic of Korea
Mahboob Alam: National Institute of Animal Science, Rural Development Admission, Cheonan 31000, Republic of Korea
Sang Min Lee: National Institute of Animal Science, Rural Development Admission, Cheonan 31000, Republic of Korea
Mi Na Park: National Institute of Animal Science, Rural Development Admission, Cheonan 31000, Republic of Korea
Ha-Seung Seong: National Institute of Animal Science, Rural Development Admission, Cheonan 31000, Republic of Korea
Min Ki Baek: ZOOTOS Co., Ltd., R&D Center, Anyang 14118, Republic of Korea
Pham Van Thuan: ZOOTOS Co., Ltd., R&D Center, Anyang 14118, Republic of Korea
Jae Gu Lee: National Institute of Animal Science, Rural Development Admission, Cheonan 31000, Republic of Korea
Seungkyu Han: ZOOTOS Co., Ltd., R&D Center, Anyang 14118, Republic of Korea

Agriculture, 2023, vol. 13, issue 12, 1-22

Abstract: Accurate weight measurement is critical for monitoring the growth and well-being of cattle. However, the traditional weighing process, which involves physically placing cattle on scales, is labor-intensive and stressful for the animals. Therefore, the development of automated cattle weight prediction techniques assumes critical significance. This study proposes a weight prediction approach for Korean cattle using 3D segmentation-based feature extraction and regression machine learning techniques from incomplete 3D shapes acquired from real farm environments. Firstly, we generated mesh data of 3D Korean cattle shapes using a multiple-camera system. Subsequently, deep learning-based 3D segmentation with the PointNet network model was employed to segment 3D mesh data into two dominant parts: torso and center body. From these segmented parts, the body length, chest girth, and chest width of Korean cattle were extracted. Finally, we implemented five regression machine learning models (CatBoost regression, LightGBM, polynomial regression, random forest regression, and XGBoost regression) for weight prediction. To validate our approach, we captured 270 Korean cattle in various poses, totaling 1190 poses of 270 cattle. The best result was achieved with mean absolute error (MAE) of 25.2 kg and mean absolute percent error (MAPE) of 5.85% using the random forest regression model.

Keywords: 3D segmentation; feature extraction; regression machine learning; weight estimation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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