Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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
ORCID logo ,
Seung Soo Lee
,
Mahboob Alam
,
Sang Min Lee
,
Mi Na Park
,
Ha-Seung Seong
,
Min Ki Baek
,
Van Thuan Pham
,
Jae Gu Lee
* ,
Seungkyu Han
*
Version 1 : Received: 6 November 2023 / Approved: 6 November 2023 / Online: 6 November 2023 (11:20:30 CET)

A peer-reviewed article of this Preprint also exists.

Dang, C.G.; Lee, S.S.; Alam, M.; Lee, S.M.; Park, M.N.; Seong, H.-S.; Baek, M.K.; Pham, V.T.; Lee, J.G.; Han, S. 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. Agriculture 2023, 13, 2266. Dang, C.G.; Lee, S.S.; Alam, M.; Lee, S.M.; Park, M.N.; Seong, H.-S.; Baek, M.K.; Pham, V.T.; Lee, J.G.; Han, S. 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. Agriculture 2023, 13, 2266.

Abstract

Accurate weight measurement is pivotal for monitoring the growth and well-being of cattle. However, the conventional weighing process, which involves physically placing cattle on scales, is labor-intensive and distressing for the animals. Hence, 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. In the initial phase, 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 two dominant parts of the cattle. From these segmented parts, three crucial dimensions 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 5.81% using the random forest regression model.

Keywords

3D segmentation; feature extraction; regression machine learning; weight estimation

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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