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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
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    Citations

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    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.

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