Body Weight Estimation of Cattle in Standing and Lying Postures Using Point Clouds Derived from Unmanned Aerial Vehicle-Based LiDAR

文献类型： 外文期刊

第一作者： Wang, Yaowu

作者： Wang, Yaowu;Kooistra, Lammert;Wang, Yaowu;Wang, Wensheng;Mucher, Sander;Wang, Wensheng

作者机构：

关键词： UAV-based LiDAR; livestock individual segmentation; body weight estimation; three-dimensional computer vision; individual identification

期刊名称：DRONES （ 影响因子：4.8； 五年影响因子：5.0 ）

ISSN：

年卷期： 2025 年 9 卷 2 期

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收录情况： SCI

摘要： This study aims to explore body weight estimation for cattle in both standing and lying postures, using 3D data. We apply a Unmanned Aerial Vehicle-based (UAV-based) LiDAR system to collect data during routine resting periods between feedings in the natural husbandry conditions of a commercial farm, which ensures minimal interruption to the animals. Ground truth data are obtained by weighing cattle as they voluntarily pass an environmentally embedded scale. We have developed separate models for standing and lying postures and trained them on features extracted from the segmented point clouds of cattle with unique identifiers (UIDs). The models for standing posture achieve high accuracy, with a best-performance model, Random Forest, obtaining an R2 of 0.94, an MAE of 4.72 kg, and an RMSE of 6.33 kg. Multiple linear regression models are trained to estimate body weight for the lying posture, using volume- and posture-wise characteristics. The model used 1 cm as the thickness of the slice-wise volume calculation, achieving an R2 of 0.71, an MAE of 7.71 kg, and an RMSE of 9.56 kg. These results highlight the potential of UAV-based LiDAR data for accurate and non-intrusive estimation of cattle body weight in lying and standing postures, which paves the way for improved management practices in precision livestock farming.

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