IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i10p1785-d1496508.html
   My bibliography  Save this article

A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats

Author

Listed:
  • Yi Sun

    (College of Information Engineering, Northwest A&F University, Yangling 712100, China)

  • Qifeng Li

    (Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Innovation Center for Digital Seed Industry, Beijing 100097, China)

  • Weihong Ma

    (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)

  • Anne De La Torre

    (Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, F-63122 Saint-Genès-Champanelle, France)

  • Simon X. Yang

    (Advanced Robotics and Intelligent Systems Laboratory, School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada)

  • Chunjiang Zhao

    (College of Information Engineering, Northwest A&F University, Yangling 712100, China
    Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance.

Suggested Citation

  • Yi Sun & Qifeng Li & Weihong Ma & Mingyu Li & Anne De La Torre & Simon X. Yang & Chunjiang Zhao, 2024. "A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats," Agriculture, MDPI, vol. 14(10), pages 1-22, October.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1785-:d:1496508
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/10/1785/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/10/1785/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1785-:d:1496508. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.