Prof .Mrs Sumedha Raut Prof. Mrs. Shubhangi Said Prof. Dr. Anand Khatri

- Efficient livestock management is critical to modern agriculture but remains labour-intensive and prone to errors. This research introduces an AI-powered real-time livestock counting and monitoring system utilizing the YOLOv9 object detection algorithm. The system addresses challenges such as dynamic farm environments, varying lighting conditions, and animal movement. It incorporates anomaly detection for behavioral and health monitoring, offering actionable insights for precision agriculture. Designed for scalability and costeffectiveness, the system is deployable on embedded platforms like Raspberry Pi. Experimental results demonstrate high accuracy, robustness, and potential to revolutionize precision agriculture, improving animal welfare and enabling data-driven decisions.

https://doi.org/10.58357/jedcs.2025.v4.i1.3