PointStack based 3D automatic body measurement for goat phenotypic information acquisition

Abstract

The body size of livestock is an essential phenotypic trait in genetic breeding, gene improvement, health screening, and animal welfare. To develop a non-contact automatic system for measuring goat body traits, we propose a point-cloud segmentation model based on an improved PointStack, which segments the automatically acquired three-dimensional (3D) point-cloud data of goats into different parts, including the head, front legs, hind legs, chest, abdomen, hip, and tail. The segmented point cloud, along with the physiological features of the goat, is then used to locate the corresponding key points for body size measurement. A novel method for key point localisation is proposed that includes coordinate normalisation, retrieval of key clusters, key point adjustment, optimisation of the traveling salesman problem, and edge detection. These methods were designed to reduce discrepancies at crucial points of body features, thereby facilitating the precise computation of the body size parameter in goats. In this work, 326 point clouds representing the upright posture of 55 goats were used for segmentation and body size measurement testing. The proposed segmentation model achieved a mean intersection over union of 89.21% and accuracy of 94.54%, outperforming comparative models. In the body traits measurement experiment, mean absolute percentage errors for body length, body height, chest width, chest girth, hip height, and hip width were recorded as 3.24%, 2.54%, 5.43%, 3.08%, 2.16%, and 4.59%, respectively. In summary, the proposed automated measurement method demonstrates high accuracy, strong robustness, and holds significant potential for widespread application.