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

Author

Listed:
  • Weihong Ma

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    These authors contributed equally to this work.)

  • Xiangyu Qi

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
    These authors contributed equally to this work.)

  • Yi Sun

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Ronghua Gao

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Luyu Ding

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Rong Wang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Cheng Peng

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Jun Zhang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Jianwei Wu

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Zhankang Xu

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Mingyu Li

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Hongyan Zhao

    (Otoke Banner Agricultural and Animal Husbandry Technology Extension Center, Ordos 016199, China)

  • Shudong Huang

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    College of Computer Science, Sichuan University, Chengdu 610065, China)

  • Qifeng Li

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

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.

Suggested Citation

  • Weihong Ma & Xiangyu Qi & Yi Sun & Ronghua Gao & Luyu Ding & Rong Wang & Cheng Peng & Jun Zhang & Jianwei Wu & Zhankang Xu & Mingyu Li & Hongyan Zhao & Shudong Huang & Qifeng Li, 2024. "Computer Vision-Based Measurement Techniques for Livestock Body Dimension and Weight: A Review," Agriculture, MDPI, vol. 14(2), pages 1-22, February.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:2:p:306-:d:1338866
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    References listed on IDEAS

    as
    1. Simianer, H., 2005. "Decision making in livestock conservation," Ecological Economics, Elsevier, vol. 53(4), pages 559-572, June.
    2. Hui Zhang & Jing Li & Tianshu Quan, 2023. "Strengthening or Weakening: The Impact of an Aging Rural Workforce on Agricultural Economic Resilience in China," Agriculture, MDPI, vol. 13(7), pages 1-16, July.
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