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

Jiawei Li, Qifeng Li, Weihong Ma, Xianglong Xue, Chunjiang Zhao, Dan Tulpan and Simon X. Yang
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Jiawei Li: College of Information and Electrical Engineering, China Agricultural University, Beijing 100091, China
Qifeng Li: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Weihong Ma: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Xianglong Xue: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Chunjiang Zhao: National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Dan Tulpan: School of Computer Science, University of Guelph, Guelph, ON N1G 2W1, Canada
Simon X. Yang: Advanced Robotics & Intelligent Systems (ARIS) Lab, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada

Agriculture, 2022, vol. 12, issue 7, 1-18

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.

Keywords: beef cattle point cloud; region segmentation; body dimension calculation; slice feature; non-contact measurement (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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