An Effective Yak Behavior Classification Model with Improved YOLO-Pose Network Using Yak Skeleton Key Points Images

Yuxiang Yang, Yifan Deng, Jiazhou Li, Meiqi Liu, Yao Yao, Zhaoyuan Peng, Luhui Gu and Yingqi Peng ()
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Yuxiang Yang: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Yifan Deng: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Jiazhou Li: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Meiqi Liu: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Yao Yao: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Zhaoyuan Peng: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Luhui Gu: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China
Yingqi Peng: College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya’an 625014, China

Agriculture, 2024, vol. 14, issue 10, 1-18

Abstract: Yak behavior is a valuable indicator of their welfare and health. Information about important statuses, including fattening, reproductive health, and diseases, can be reflected and monitored through several indicative behavior patterns. In this study, an improved YOLOv7-pose model was developed to detect six yak behavior patterns in real time using labeled yak key-point images. The model was trained using labeled key-point image data of six behavior patterns including walking, feeding, standing, lying, mounting, and eliminative behaviors collected from seventeen 18-month-old yaks for two weeks. There were another four YOLOv7-pose series models trained as comparison methods for yak behavior pattern detection. The improved YOLOv7-pose model achieved the best detection performance with precision, recall, mAP0.5, and mAP0.5:0.95 of 89.9%, 87.7%, 90.4%, and 76.7%, respectively. The limitation of this study is that the YOLOv7-pose model detected behaviors under complex conditions, such as scene variation, subtle leg postures, and different light conditions, with relatively lower precision, which impacts its detection performance. Future developments in yak behavior pattern detection will amplify the simple size of the dataset and will utilize data streams like optical and video streams for real-time yak monitoring. Additionally, the model will be deployed on edge computing devices for large-scale agricultural applications.

Keywords: improved YOLOv7-pose; yak; image processing; deep learning (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: 2024
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