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A multi-posture adaptive method for measuring goat bodies dimensions using 3D point clouds in real-world applications

文献类型： 外文期刊

作者： Sun, Yi 1 ; Li, Qifeng 2 ; Ma, Weihong 2 ; Morris, Daniel 4 ; Guo, Hao 5 ; Qi, Xiangyu 5 ; Li, Mingyu 2 ; Zhao, Chunjiang 1 ;

作者机构： 1.Northwest A&F Univ, Coll Informat Engn, Yangling 712100, Shaanxi, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

3.Natl Innovat Ctr Digital Seed Ind, Beijing 100097, Peoples R China

4.Michigan State Univ, Biosyst & Agr Engn, E Lansing, MI 48824 USA

5.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

关键词： Target extraction; 3D reconstruction; Region segmentation; Eliminating posture interference; Body dimension calculation

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：5.3； 五年影响因子：5.9 ）

ISSN： 1537-5110

年卷期： 2025 年 257 卷

页码：

收录情况： SCI

摘要： Body dimensions are recognised as critical indicators in the processes of fattening and breeding goats. However, manual measurement methods are both time-consuming and labour-intensive, often inducing significant stress in the animals and compromising their healthy growth. To address these concerns, a non-contact, high-throughput body measurement system for goats has been developed utilising multi-view three-dimensional point clouds. It employs an arched-channel device equipped with three depth cameras to collect three-dimensional point clouds. Firstly, a Multi-View Filter Reconstruction Processing Pipeline is proposed to filter, down-sample, segment, register, extract and reconstruct the surface of the target point cloud. Secondly, a Directional Positioning Continuous Segmentation Algorithm is introduced to identify key regions of the goat's body for precise segmentation. Finally, body dimensions are calculated from the identified key regions. The average error rates of various measurements, including body height, body oblique length, chest girth, chest depth, chest width, abdominal girth, abdominal depth, and abdominal width are 1.30 %, 2.73 %, 1.89 %, 2.43 %, 3.37 %, 2.11 %, 2.94 %, and 2.59 %, respectively. This algorithm provides a scientific and accurate way to measure the key body dimensions of goats. This algorithm demonstrates generalisability and robustness, being unaffected by environmental conditions or the diverse postures of the goats. This automated method for measuring goat body dimensions offers an efficient means of collecting phenotypic data during goat fattening, providing a convenient approach to precise and high-throughput body dimension assessments in goat breeding.

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