A New Approach to Animal Behavior Classification using Recurrent Neural Networks

Abstract

The behavior of cattle holds valuable insights into their health, well-being, and productivity. Accurately classifying their diverse actions has become increasingly important in modern precision livestock farming. This study explores the efficiency of recurrent neural networks (RNNs) in classifying cow behavior using time series data from tri-axial accelerometer sensors. The goal is to detect the complex patterns embedded in the cattle motion data, providing an accurate understanding of their behavior. The proposed methodology uses the inherent sequential nature of time series data to distinguish and categorize diverse behaviors of cows. The performance of the proposed model is evaluated on a comprehensive dataset capturing various behaviors, including feeding, resting, ruminating, moving, salting, and other behaviors. The results demonstrate the model's ability to accurately classify cow behavior with high precision and recall, highlighting the potential of RNNs for automated behavior monitoring in cattle farms. This opens doors for improved animal welfare, better productivity, optimized management practices, and enhanced decision-making in the livestock industry.