Advancing standardized cattle behavior classification with a random forest model evaluated across diverse datasets

The lack of precise, autonomous tools for monitoring and classifying cattle behavior limits farmers’ ability to make proactive and informed decisions regarding grazing and herd management. Currently, there is no standardized model for cattle behavior classification. To address this gap, we developed and evaluated a Random Forest machine learning model using accelerometer data collected from multiple sensor placements on the cow’s body (nose, ear, neck). Our model integrates open-source datasets with newly collected field data and classifies key behaviors such as eating, rumination, and movement. Unlike prior studies focused on single-herd or controlled environments, this work demonstrates that a single, standardized model architecture and feature pipeline can be applied consistently across five cattle behavior datasets, spanning different breeds, environments, and devices. Cross-dataset transfer experiments were conducted as an exploratory analysis to assess robustness rather than as a primary performance criterion. The model achieves high accuracy—meeting or exceeding published benchmarks—while maintaining low computational demands, making it practical for real-time applications such as virtual fencing. This scalable, data-driven model supports precision livestock monitoring by enabling accurate, real-time classification of key cattle behaviors from wearable sensor data across diverse datasets, facilitating automated and data-informed livestock management.