Monitoring Cattle Ruminating Behavior Based on an Improved Keypoint Detection Model

Author:

Li Jinxing1234,Liu Yanhong1234,Zheng Wenxin5,Chen Xinwen5,Ma Yabin6,Guo Leifeng2ORCID

Affiliation:

1. College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China

2. Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China

3. Xinjiang Agricultural Informatization Engineering Technology Research Center, Urumqi 830052, China

4. Ministry of Education Engineering Research Centre for Intelligent Agriculture, Urumqi 830052, China

5. Institute of Animal Husbandry Quality Standards, Xinjiang Academy of Animal Science, Urumqi 830011, China

6. Hebei Animal Husbandry and Breeding Work Station, Shijiazhuang 050049, China

Abstract

Cattle rumination behavior is strongly correlated with its health. Current methods often rely on manual observation or wearable devices to monitor ruminating behavior. However, the manual monitoring of cattle rumination is labor-intensive, and wearable devices often harm animals. Therefore, this study proposes a non-contact method for monitoring cattle rumination behavior, utilizing an improved YOLOv8-pose keypoint detection algorithm combined with multi-condition threshold peak detection to automatically identify chewing counts. First, we tracked and recorded the cattle’s rumination behavior to build a dataset. Next, we used the improved model to capture keypoint information on the cattle. By constructing the rumination motion curve from the keypoint information and applying multi-condition threshold peak detection, we counted the chewing instances. Finally, we designed a comprehensive cattle rumination detection framework to track various rumination indicators, including chewing counts, rumination duration, and chewing frequency. In keypoint detection, our modified YOLOv8-pose achieved a 96% mAP, an improvement of 2.8%, with precision and recall increasing by 4.5% and 4.2%, enabling the more accurate capture of keypoint information. For rumination analysis, we tested ten video clips and compared the results with actual data. The experimental results showed an average chewing count error of 5.6% and a standard error of 2.23%, verifying the feasibility and effectiveness of using keypoint detection technology to analyze cattle rumination behavior. These physiological indicators of rumination behavior allow for the quicker detection of abnormalities in cattle’s rumination activities, helping managers make informed decisions. Ultimately, the proposed method not only accurately monitors cattle rumination behavior but also provides technical support for precision management in animal husbandry, promoting the development of modern livestock farming.

Funder

Key Research and Development Plan of Hebei Province

Key Research and Development Plan of Xinjiang Uygur Autonomous Region

Shijiazhuang Key Research and Development Special Project

Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences

Publisher

MDPI AG

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