Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods

文献类型： 外文期刊

第一作者： Li, Yongfeng

作者： Li, Yongfeng;Shu, Hang;Xu, Beibei;Zhang, Wenju;Jin, Zhongming;Guo, Leifeng;Wang, Wensheng;Li, Yongfeng;Shu, Hang;Bindelle, Jerome

作者机构：

关键词： behavior classification; inertial measurement units; machine learning; precision livestock farming

期刊名称：ANIMALS （ 影响因子：3.231； 五年影响因子：3.312 ）

ISSN： 2076-2615

年卷期： 2022 年 12 卷 9 期

页码：

收录情况： SCI

摘要： Simple Summary Traditionally, farmers are unable to pay enough attention to individual livestock. An increasing number of sensors are being used to monitor animal behavior, early disease detection, and evaluation of animal welfare. In this study, we used machine learning algorithms to identify multiple unitary behaviors and movements of dairy cattle recorded by motion sensors. We also investigated the effect of time window on the performance of unitary behaviors classification and discussed the necessity of movement analysis. This study shows a feasible way to explore more detailed movements based on the result of unitary behaviors classification. Low-cost sensors provide remote monitoring of animal behaviors to help producers comprehensively and accurately identify the health status of individual livestock in real-time. The behavior of livestock on farms is the primary representation of animal welfare, health conditions, and social interactions to determine whether they are healthy or not. The objective of this study was to propose a framework based on inertial measurement unit (IMU) data from 10 dairy cows to classify unitary behaviors such as feeding, standing, lying, ruminating-standing, ruminating-lying, and walking, and identify movements during unitary behaviors. Classification performance was investigated for three machine learning algorithms (K-nearest neighbors (KNN), random forest (RF), and extreme boosting algorithm (XGBoost)) in four time windows (5, 10, 30, and 60 s). Furthermore, feed tossing, rolling biting, and chewing in the correctly classified feeding segments were analyzed by the magnitude of the acceleration. The results revealed that the XGBoost had the highest performance in the 60 s time window with an average F1 score of 94% for the six unitary behavior classes. The F1 score of movements is 78% (feed tossing), 87% (rolling biting), and 87% (chewing). This framework offers a possibility to explore more detailed movements based on the unitary behavior classification.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序