Benchmarking YOLOv8-v13 Architectures for Intelligent Real-Time Cattle Monitoring and Data-Driven Farm Management in Precision Livestock Farming

Real-time and accurate cattle behavior detection is crucial for improving animal welfare and optimizing livestock management. To address this, this study comprehensively evaluates six community-developed lightweight YOLO (You Only Look Once) models-YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n, and YOLOv13n-for recognizing five key cattle behaviors (standing, lying, foraging, drinking, and rumination) using the custom CBVD-5 dataset. The dataset includes diverse lighting conditions and natural barn environments to reflect real-world monitoring scenarios. All models were assessed using standard object detection metrics (precision, recall, mAP@50, and mAP@50-95) and deployment-oriented indicators including inference time, frames per second (FPS), model size, and training duration to provide a balanced evaluation of accuracy and efficiency. YOLOv13n achieved the highest mAP@50-95 (0.712), while YOLOv11n reached the highest mAP@50 (0.967) and exhibited stable convergence with a good trade-off between detection accuracy and inference speed. YOLOv10n demonstrated the fastest inference speed (1.2 ms/frame, ~833 FPS), making it well-suited for latency-sensitive applications such as continuous barn surveillance. All models maintained compact sizes below 6 MB and real-time throughput exceeding ~300 FPS, confirming their practicality for edge deployment. The results establish a reproducible benchmarking framework for evaluating modern YOLO variants, offering insights into model selection for efficient, scalable, and welfare-oriented livestock monitoring systems.