IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v7y2025i2p986-1005.html

Role of Machine Learning in Livestock Health Monitoring System: A Systematic Literature Review

Author

Listed:
  • Muhammad Mohsin Raza,Rabia Tehseen,Uzma Omer,Muhammad Qasim,Usman Aamer,Ramsha Saeed,Muhammad Farrukh Khan

    (Department of Computer Science,University of Central Punjab, Lahore,Pakistan.Department of Computer Science,University of Management&Technology,Lahore,Pakistan.Department of Computer Science, University of Education, Lahore, Pakistan.Department of Computing, NASTP Institute of Information Technology, Lahore, Pakistan)

Abstract

Machine Learning (ML) can significantly enhance livestock management in various ways by providing real-time insights into animal health, behavior, and well-being. Livestock production, monitoring, and management can be revolutionized by using ML techniques. This study presents a comprehensive review of the literature regarding IoT devices used for monitoring cattle health, key characteristics of these devices, wearable technology used, sensors, and ML algorithms. In order to complete the review, a thorough examination and synthesis of the research articles published in reputable research venues between 2018 and 2023 are conducted. The findings revealed that pressure and pulse-rate sensors are the most often utilized types for recording the health status of animals experiencing health issues.

Suggested Citation

  • Muhammad Mohsin Raza,Rabia Tehseen,Uzma Omer,Muhammad Qasim,Usman Aamer,Ramsha Saeed,Muhammad Farrukh Khan, 2025. "Role of Machine Learning in Livestock Health Monitoring System: A Systematic Literature Review," International Journal of Innovations in Science & Technology, 50sea, vol. 7(2), pages 986-1005, May.
    • Handle: RePEc:abq:ijist1:v:7:y:2025:i:2:p:986-1005
    as

    Download full text from publisher

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/1406/1912
    Download Restriction: no
    File URL: https://journal.50sea.com/index.php/IJIST/article/view/1406
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuan Rao & Min Jiang & Wen Wang & Wu Zhang & Ruchuan Wang, 2020. "On-farm welfare monitoring system for goats based on Internet of Things and machine learning," International Journal of Distributed Sensor Networks, , vol. 16(7), pages 15501477209, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      More about this item

      Keywords

      ;
      ;
      ;
      ;
      ;
      ;
      ;
      ;

      Statistics

      Access and download statistics

      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:abq:ijist1:v:7:y:2025:i:2:p:986-1005. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Iqra Nazeer (email available below). General contact details of provider: .

      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.