A Livestock Monitoring System and Machine Learning Sequence Classification Comparison for Behavior Classification

• Luiz Ricardo da Silveira UNICAMP
• João Roberto Bertini Junior UNICAMP

Resumo

Timely disease diagnosis is essential for reducing livestock mortality, as delays caused by reliance on human visual inspection can lead to severe consequences. Early detection of illness hinges on access to detailed, individual animal data capable of capturing subtle behavioral and physiological changes before visible symptoms emerge. A robust classification system is therefore critical for automating behavior analysis, enabling prompt identification of deviations from normal patterns that may indicate illness and facilitating timely intervention. This work reports the development of an innovative livestock behavior classification and health monitoring device. The system incorporates a wireless neck collar equipped with motion and temperature sensors for continuous animal activity monitoring. A prototype collar was tested on two heifers in a controlled field setting under video surveillance. Collected data were manually annotated by human observers, classifying the heifers’ behaviors into five key categories: eating, drinking, walking, lying down, and standing. The study covers the entire process of creating the monitoring system, from the design and construction of the neck collar to data collection and annotation. It also details the complete data mining workflow, including data cleaning, pre-processing, and comparative analysis using six shallow Machine Learning (ML) algorithms. The results indicate that Gradient Tree Boosting is the best-performing model based on classification metrics appropriate for multiclass imbalanced data, such as the F1-score, Weighted F1-score, and Kappa coefficient. The designed device, integrated with a ML model, proved to be an effective and low-cost approach for classifying livestock behavior.