A comparative study on time series data-based artificial intelligence approaches for classifying cattle feeding behavior

Abstract

Cattle feeding behavior analysis is crucial for optimizing livestock management practices and ensuring animal well-being. This study presents a comparative analysis of three models: two machine learning algorithms including random forest and support vector machine (SVM), in addition to a deep learning convolutional neural networks (CNN) model, for classifying cattle feeding behaviors (eating, ruminating, and other) using time series data generated from a 3-axis accelerometer. The results of this study highlight the performance of these methods in accurately categorizing cattle feeding behaviors and demonstrate the importance of precise and efficient livestock monitoring and contributing to the improvement of animal well-being and enhancing the overall effectiveness of livestock operations.