Li Y, Shu H, Bindelle J, Xu B, Zhang W, Jin Z, Guo L, Wang W. Classification and Analysis of Multiple Cattle Unitary Behaviors and Movements Based on Machine Learning Methods. Animals. 2022;12(9):1060. doi:10.3390/ani12091060.
This study proposes a machine learning framework to classify dairy cattle behaviors using inertial measurement unit (IMU) data from 10 cows. The algorithms tested—K-nearest neighbors (KNN), random forest (RF), and extreme boosting (XGBoost)—showed XGBoost performed best with a 94% F1 score in a 60-second time window. Movements such as feed tossing, rolling biting, and chewing were also analyzed, achieving F1 scores of 78%, 87%, and 87%, respectively. This framework supports detailed behavior and movement analysis, offering potential for improved health and welfare monitoring in livestock.
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