IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v16y2020i7p1550147720944030.html
   My bibliography  Save this article

On-farm welfare monitoring system for goats based on Internet of Things and machine learning

Author

Listed:
  • Yuan Rao
  • Min Jiang
  • Wen Wang
  • Wu Zhang
  • Ruchuan Wang

Abstract

Intensive animal husbandry is becoming more and more popular with the adoption of modern livestock farming technologies. In such circumstances, it is required that the welfare of animals be continuously monitored in a real-time way. To this end, this study describes one on-farm welfare monitoring system for goats, with a combination of Internet of Things and machine learning. First, the system was designed for uninterruptedly monitoring goat growth in a multifaceted and multilevel manner, by means of collecting on-farm videos and representative environmental data. Second, the monitoring hardware and software systems were presented in detail, aiming at supporting remote operation and maintenance, and convenience for further development. Third, several key approaches were put forward, including goat behavior analysis, anomaly data detection, and processing based on machine learning. Through practical deployment in the real situation, it was demonstrated that the developed system performed well and had good potential for offering real-time monitoring service for goats’ welfare, with the help of accurate environmental data and analysis of goat behavior.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:intdis:v:16:y:2020:i:7:p:1550147720944030
    DOI: 10.1177/1550147720944030
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147720944030
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147720944030?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:sae:intdis:v:16:y:2020:i:7:p:1550147720944030. 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: SAGE Publications (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.