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Detection of Respiratory Rate of Dairy Cows Based on Infrared Thermography and Deep Learning

Author

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  • Kaixuan Zhao

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China
    Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471023, China)

  • Yijie Duan

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China
    Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471023, China)

  • Junliang Chen

    (College of Food & Bioengineering, Henan University of Science and Technology, Luoyang 471023, China)

  • Qianwen Li

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China)

  • Xing Hong

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China
    Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471023, China)

  • Ruihong Zhang

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China
    Science & Technology Innovation Center for Completed Set Equipment, Longmen Laboratory, Luoyang 471023, China)

  • Meijia Wang

    (School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, China)

Abstract

The respiratory status of dairy cows can reflect their heat stress and health conditions. It is widely used in the precision farming of dairy cows. To realize intelligent monitoring of cow respiratory status, a system based on infrared thermography was constructed. First, the YOLO v8 model was used to detect and track the nose of cows in thermal images. Three instance segmentation models, Mask2Former, Mask R-CNN and SOLOv2, were used to segment the nostrils from the nose area. Second, the hash algorithm was used to extract the temperature of each pixel in the nostril area of a cow to obtain the temperature change curve. Finally, the sliding window approach was used to detect the peaks of the filtered temperature curve to obtain the respiratory rate of cows. Totally 81 infrared thermography videos were used to test the system, and the results showed that the AP 50 of nose detection reached 98.6%, and the AP 50 of nostril segmentation reached 75.71%. The accuracy of the respiratory rate was 94.58%, and the correlation coefficient R was 0.95. Combining infrared thermography technology with deep learning models can improve the accuracy and usability of the respiratory monitoring system for dairy cows.

Suggested Citation

  • Kaixuan Zhao & Yijie Duan & Junliang Chen & Qianwen Li & Xing Hong & Ruihong Zhang & Meijia Wang, 2023. "Detection of Respiratory Rate of Dairy Cows Based on Infrared Thermography and Deep Learning," Agriculture, MDPI, vol. 13(10), pages 1-15, October.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:10:p:1939-:d:1253465
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    Cited by:

    1. Peng Ni & Shiqi Hu & Yabo Zhang & Wenyang Zhang & Xin Xu & Yuheng Liu & Jiale Ma & Yang Liu & Hao Niu & Haipeng Lan, 2024. "Design and Optimization of Key Parameters for a Machine Vision-Based Walnut Shell–Kernel Separation Device," Agriculture, MDPI, vol. 14(9), pages 1-18, September.

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