Research and Preliminary Evaluation of Key Technologies for 3D Reconstruction of Pig Bodies Based on 3D Point Clouds

Kaidong Lei, Xiangfang Tang (), Xiaoli Li, Qinggen Lu, Teng Long, Xinghang Zhang and Benhai Xiong
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Kaidong Lei: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Xiangfang Tang: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Xiaoli Li: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Qinggen Lu: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Teng Long: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Xinghang Zhang: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
Benhai Xiong: State Key Laboratory of Animal Nutrition, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China

Agriculture, 2024, vol. 14, issue 6, 1-12

Abstract: In precision livestock farming, the non-contact perception of live pig body measurement data is a critical technological branch that can significantly enhance breeding efficiency, improve animal welfare, and effectively prevent and control diseases. Monitoring pig body measurements allows for accurate assessment of their growth and production performance. Currently, traditional sensing methods rely heavily on manual measurements, which not only have large errors and high workloads but also may cause stress responses in pigs, increasing the risk of African swine fever, and its costs of prevention and control. Therefore, we integrated and developed a system based on a 3D reconstruction model that includes the following contributions: 1. We developed a non-contact system for perceiving pig body measurements using a depth camera. This system, tailored to the specific needs of laboratory and on-site pig farming processes, can accurately acquire pig body data while avoiding stress and considering animal welfare. 2. Data preprocessing was performed using Gaussian filtering, mean filtering, and median filtering, followed by effective estimation of normals using methods such as least squares, principal component analysis (PCA), and random sample consensus (RANSAC). These steps enhance the quality and efficiency of point cloud processing, ensuring the reliability of 3D reconstruction tasks. 3. Experimental evidence showed that the use of the RANSAC method can significantly speed up 3D reconstruction, effectively reconstructing smooth surfaces of pigs. 4. For the acquisition of smooth surfaces in 3D reconstruction, experimental evidence demonstrated that the RANSAC method significantly improves the speed of reconstruction. 5. Experimental results indicated that the relative errors for chest girth and hip width were 3.55% and 2.83%, respectively. Faced with complex pigsty application scenarios, the technology we provided can effectively perceive pig body measurement data, meeting the needs of modern production.

Keywords: 3D point clouds; data processing; 3D reconstruction of pig bodies; body automated monitoring; precision livestock farming (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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