Automated cattle segmentation from UAV-based LiDAR point clouds

Effective segmentation of individual animals is crucial for monitoring cattle growth in precision livestock farming(PLF) when employing computer vision (CV) on 2D images or videos. However, similar research in 3D scenes has been an area with limited prior exploration. This study applied three-dimensional (3D) CV approaches in conjunction with a UAV-based LiDAR system, enabling automated 3D individual segmentation. During fieldwork, the UAV-based LiDAR system scanned an outdoor area where a herd of 96 male Simmental beef cattle rested after feeding, executing 36 flight plans. These plans resulted in 36 campaign point clouds, each obtained under diverse flight conditions encompassing a range of heights (8m, 10m, 15m, 30m, 50m) and speeds (1m/s, 2m/s, 3m/s, 5m/s, 7m/s, 9m/s). Of these, 30 were conducted in daytime light conditions, while 6 were made in nighttime conditions. To segment the point clouds for individual animal detection, a height difference threshold is employed to distinguish between lying and standing cattle. This is followed by the DBSCAN clustering algorithm to effectively separate standing animals, using the above-threshold portion. Finally, noise removal is employed to ensure complete cattle body point clouds. This procedure yielded 276 individual cattle point clouds from 400 standing animals, including 47 from nighttime scans. The study confirms the effectiveness of 3D individual segmentation through automatically segmenting standing cattle under natural husbandry conditions from UAV-based LiDAR point clouds. The method facilitates 3D cattle growth monitoring research such as body measurement assessment and body weight estimation in PLF.