文献类型： 外文期刊

作者： Li, Jinxing ¹ ; Liu, Yanhong ¹ ; Zheng, Wenxin ⁵ ; Chen, Xinwen ⁵ ; Ma, Yabin ⁶ ; Guo, Leifeng ² ;

作者机构： 1.Xinjiang Agr Univ, Coll Comp & Informat Engn, Urumqi 830052, Peoples R China

2.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100080, Peoples R China

3.Xinjiang Agr Univ, Xinjiang Agr Informatizat Engn Technol Res Ctr, Urumqi 830052, Peoples R China

4.Minist Educ, Engn Res Ctr Intelligent Agr, Urumqi 830052, Peoples R China

5.Xinjiang Acad Anim Sci, Inst Anim Husb Qual Stand, Urumqi 830011, Peoples R China

6.Hebei Anim Husb & Breeding Work Stn, Shijiazhuang 050049, Peoples R China

关键词： cattle; improved YOLOv8-pose; rumination behavior; peak detection

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.0 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 12 期

页码：

收录情况： SCI

摘要： Simple Summary Rumination behavior is a crucial indicator of cattle health and welfare. The timely monitoring and analysis of this behavior can provide valuable insights into the physiological status of the animals. Distinguishing from manual observation and wearable devices, this study proposes a method using video technology for monitoring cattle rumination behavior. This method aims to track physiological indicators during rumination, including chewing count, rumination duration, and chewing frequency. This approach can help livestock managers promptly understand the health status of cattle. Furthermore, this research method offers a new perspective for the construction of smart farming, providing technical support for the intelligent transformation of the livestock industry.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.