Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review

文献类型： 外文期刊

第一作者： Ma, Weihong

作者： Ma, Weihong;Qi, Xiangyu;Sun, Yi;Gao, Ronghua;Ding, Luyu;Wang, Rong;Peng, Cheng;Zhang, Jun;Wu, Jianwei;Xu, Zhankang;Li, Mingyu;Huang, Shudong;Li, Qifeng;Qi, Xiangyu;Zhao, Hongyan;Huang, Shudong

作者机构：

关键词： 3D reconstruction; stressless body dimension measurement; visual weight estimation; precision livestock farming

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2024 年 14 卷 2 期

页码：

收录情况： SCI

摘要： Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序