Non-contact respiration rate measurement of multiple cows in a free-stall barn using computer vision methods

文献类型： 外文期刊

第一作者： Shu, Hang

作者： Shu, Hang;Gu, Xianhong;Shu, Hang;Shu, Hang;Bindelle, Jerome

作者机构：

关键词： Precision livestock farming; Animal welfare; Deep learning; Multi-object measurement; Heat stress

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 218 卷

页码：

收录情况： SCI

摘要： In cattle, respiration rate (RR) provides researchers and practitioners with valuable physiological information. However, traditional visual observation requires massive labour and is impractical for large-scale commercial farms. Recently developed vision -based RR measurement methods are highly limited to measuring a small number of animals in a controlled environment. Therefore, this paper aimed to propose a vision -based multiobject RR measurement method for dairy cows lying in free stalls. An RGB camera was aimed at the lying zone and was able to cover about 16 stalls. The proposed framework first utilised two YOLOv5-based networks to segment cow instances and detect cow flank objects. Next, an object tracker was used to link the predictions of each cow throughout the video clip. The Lucas-Kanade optical flow was then calculated specifically on the overlapped area of the cow mask and the flank bounding box. Finally, RR was extracted using Fast Fourier Transform. The results show that the proposed method had a precise RR measurement with a correlation coefficient of 0.944, a root mean square error of 5.35 breaths per minute, and an intraclass correlation coefficient of 0.974 when compared to visual observation. The piecewise regression models identified a change in RR when the ambient temperature reached 23.6 degrees C or the temperature -humidity index reached 72. The corresponding RR thresholds were 60.9 and 60.2 breaths per minute, respectively. Collectively, these results can be used to inform an automated local cooling system, e.g., fans in the lying area. However, more experiments and calibration with data collected using more cost-effective video recording systems are required before this technology can be applied on farms.

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