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

Kaixuan Zhao, Yijie Duan, Junliang Chen, Qianwen Li (), Xing Hong, Ruihong Zhang and Meijia Wang
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Kaixuan Zhao: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China
Yijie Duan: College of Agricultural Equipment Engineering, Henan University of Science and Technology, 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
Ruihong Zhang: College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471023, China
Meijia Wang: School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi’an 710021, China

Agriculture, 2023, vol. 13, issue 10, 1-15

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.

Keywords: infrared thermography; respiratory rate; nostril segmentation; dairy cows (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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