IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i2p385-d1059302.html
   My bibliography  Save this article

A Non-Contact Cow Estrus Monitoring Method Based on the Thermal Infrared Images of Cows

Author

Listed:
  • Zhen Wang

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
    These authors contributed equally to this work.)

  • Shuai Wang

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China
    These authors contributed equally to this work.)

  • Chunguang Wang

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China)

  • Yong Zhang

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China)

  • Zheying Zong

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China)

  • Haichao Wang

    (College of Energy and Transportation Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China)

  • Lide Su

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China)

  • Yingjie Du

    (College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Inner Mongolia Autonomous Region, Hohhot 010018, China)

Abstract

Traditional methods of cow estrus monitoring technology are not suitable for the current needs of large-scale, intensive and welfare-based farming. There is a need to improve the detection rate of cow estrus and to reduce the emergency response caused by wearing contact devices. Furthermore, it is necessary to verify the practical effectiveness of the LOGISITC and SV (support vector machine) models for modeling cow estrus. In this paper, we have proposed a non-contact cow estrus monitoring method based on the thermal infrared images of cows and have proposed a lab-color-space-based feature extraction method for the thermal infrared images of cow eyes and vulvas. The test subjects were 10 Holstein cows, monitored on a fixed basis, to determine the best segmentation contour. The LOGISTIC and SVM (support vector machine) models were used to establish the cow estrus model using the thermal infrared temperature variation in cows in estrus and cows not in estrus. The experimental results showed that the heat detection rate of the LOGISTIC-based model was 82.37% and the heat detection rate of the SVM-based model was 81.42% under the optimal segmentation profile. The highest temperature in the eye and vulva of cows was the input, and the recall rate was above 86%. The heat monitoring method based on thermal infrared images does not cause stress to cows and meets the needs of modern dairy farming for welfare breeding.

Suggested Citation

  • Zhen Wang & Shuai Wang & Chunguang Wang & Yong Zhang & Zheying Zong & Haichao Wang & Lide Su & Yingjie Du, 2023. "A Non-Contact Cow Estrus Monitoring Method Based on the Thermal Infrared Images of Cows," Agriculture, MDPI, vol. 13(2), pages 1-19, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:385-:d:1059302
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/2/385/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/2/385/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Na Liu & Jingwei Qi & Xiaoping An & Yuan Wang, 2023. "A Review on Information Technologies Applicable to Precision Dairy Farming: Focus on Behavior, Health Monitoring, and the Precise Feeding of Dairy Cows," Agriculture, MDPI, vol. 13(10), pages 1-21, September.

    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:13:y:2023:i:2:p:385-:d:1059302. 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.