A Non-Contact and Fast Estimating Method for Respiration Rate of Cows Using Machine Vision

Xiaoshuai Wang, Binghong Chen, Ruimin Yang, Kai Liu, Kaixuan Cuan () and Mengbing Cao ()
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Xiaoshuai Wang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Binghong Chen: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Ruimin Yang: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Kai Liu: Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong SAR, China
Kaixuan Cuan: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Mengbing Cao: Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, Hangzhou 310058, China

Agriculture, 2023, vol. 14, issue 1, 1-15

Abstract: Detecting respiration rate (RR) is a promising and practical heat stress diagnostic method for cows, with significant potential benefits for dairy operations in monitoring thermal conditions and managing cooling treatments. Currently, the optical flow method is widely employed for automatic video-based RR estimation. However, the optical flow-based approach for RR estimation can be time-consuming and susceptible to interference from various unrelated cow movements, such as rising, lying down, and body shaking. The aim of this study was to propose a novel optical flow-based algorithm for remotely and rapidly detecting the respiration rate of cows in free stalls. To accomplish this, we initially collected 250 sixty-second video episodes from a commercial dairy farm, which included some episodes with interfering motions. We manually observed the respiration rate for each episode, considering it as the ground truth RR. The analysis revealed that certain cow movements, including posture changes and body shaking, introduced noise that compromises the precision of RR detection. To address this issue, we implemented noise filters, with the Butterworth filter proving highly effective in mitigating noise resulting from cow movements. The selection of the region of interest was found to have a substantial impact on the accuracy of RR detection. Opting for the central region was recommended for optimal results. The comparison between the RR estimated by the modified cow respiration rate (MCRR) algorithm and the ground truth RR showed a good agreement with a mean absolute relative error of 7.6 ± 8.9% and a Pearson correlation coefficient of 0.86. Additionally, the results also indicated that reducing the original frame rate from 25 to 5 frames per second and adjusting the image pixel size from 630 × 450 to 79 × 57 pixels notably reduced computational time from 39.8 to 2.8 s, albeit with a slight increase in mean absolute relative error to 8.0 ± 9.0%.

Keywords: body shake; dairy cows; heat stress; nearly real-time estimation; optical flow method; posture change (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
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