Key Region Extraction and Body Dimension Measurement of Beef Cattle Using 3D Point Clouds

Abstract

Body dimensions are key indicators for the beef cattle fattening and breeding process. On-animal measurement is relatively inefficient, and can induce severe stress responses among beef cattle and pose a risk for operators, thereby impacting the cattle’s growth rate and wellbeing. To address the above issues, a highly efficient and automatic method was developed to measure beef cattle’s body dimensions, including the oblique length, height, width, abdominal girth, and chest girth, based on the reconstructed three-dimensional point cloud data. The horizontal continuous slice sequence of the complete point clouds was first extracted, and the central point of the beef cattle leg region was determined from the span distribution of the point cloud clusters in the targeted slices. Subsequently, the boundary of the beef cattle leg region was identified by the “five-point clustering gradient boundary recognition algorithm” and was then calibrated, followed by the accurate segmentation of the corresponding region. The key regions for body dimension data calculation were further determined by the proposed algorithm, which forms the basis of the scientific calculation of key body dimensions. The influence of different postures of beef cattle on the measurement results was also preliminarily discussed. The results showed that the errors of calculated body dimensions, i.e., the oblique length, height, width, abdominal girth, and chest girth, were 2.3%, 2.8%, 1.6%, 2.8%, and 2.6%, respectively. In the present work, the beef cattle body dimensions could be effectively measured based on the 3D regional features of the point cloud data. The proposed algorithm shows a degree of generalization and robustness that is not affected by different postures of beef cattle. This automatic method can be effectively used to collect reliable phenotype data during the fattening of beef cattle and can be directly integrated into the breeding process.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey

“…While this method is straightforward, its limitation lies in the fact that only one side of the cattle body is captured in the acquired point cloud data. Li et al (2022) [ 66 ] developed a fully automated method for measuring beef cattle’s body dimensions. This approach not only achieved a high accuracy and speed in measurement but also covered a comprehensive set of measurement locations, making it a practical solution for cattle body measurement with significant commercial potential.…”

Recent Advancements in Precision Livestock Farming

“…Tests on farm conditions reported an accuracy as high as 91.6% on weight measurements based on the proposed model. Furthermore, a highly efficient and automatic method was developed by Li et al [12] to measure beef cattle's body dimensions based on the reconstructed three-dimensional point cloud. The results showed that the errors of calculated body dimensions, i.e., the oblique length, height, width, abdominal girth, and chest girth, were always lower than 3%.…”