IDEAS home Printed from https://ideas.repec.org/a/ajp/edwast/v8y2024i6p6416-6430id3396.html
   My bibliography  Save this article

Role of computer vision and deep learning algorithms in livestock behavioural recognition: A state-of-the-art- review

Author

Listed:
  • Olawuyi Fatoki
  • Chunling Tu
  • Robert Hans
  • Rotimi-Williams Bello

Abstract

The increasing demand for sustainable livestock products necessitates a re-evaluation of animal production and breeding practices. Contemporary breeding programs now integrate animal phenotypic behaviors due to their considerable influence on productivity, health, and welfare, which ultimately impact industry yield and economic outcomes. Monitoring animal behavior manually is challenging and subjective, especially in continuous or large-scale operations, as it is time-consuming and labor-intensive. Consequently, computer vision technology has attracted attention for its objectivity, non-invasiveness, and capacity for continuous monitoring. However, recognizing livestock behavior using computer vision remains difficult due to complex scenes and varying conditions, hindering its widespread adoption in the industry. Deep learning technology has emerged as a promising solution, mitigating some of these challenges and enhancing the recognition of livestock behaviors. This paper reviews recent advancements in computer vision methods for detecting behaviors in livestock such as cattle with an emphasis on behaviors critical for health, welfare, and productivity. It investigates the development of both traditional computer vision and deep learning techniques for image segmentation, identification, and behavior recognition. The review explores the development of research trends in livestock behavior recognition, focusing on improvements in reliable identification algorithms, the analysis of behaviors at different growth stages, the measurement of behavioral data, and the design of systems to evaluate welfare, health, growth, and development.

Suggested Citation

  • Olawuyi Fatoki & Chunling Tu & Robert Hans & Rotimi-Williams Bello, 2024. "Role of computer vision and deep learning algorithms in livestock behavioural recognition: A state-of-the-art- review," Edelweiss Applied Science and Technology, Learning Gate, vol. 8(6), pages 6416-6430.
  • Handle: RePEc:ajp:edwast:v:8:y:2024:i:6:p:6416-6430:id:3396
    as

    Download full text from publisher

    File URL: https://learning-gate.com/index.php/2576-8484/article/view/3396/1273
    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:ajp:edwast:v:8:y:2024:i:6:p:6416-6430:id:3396. 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: Melissa Fernandes (email available below). General contact details of provider: https://learning-gate.com/index.php/2576-8484/ .

    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.