Advancing AI-Powered Real-Time Livestock Management: An Optimized YOLOv9-Based Approach - Meher, Sumedha; Said, Shubhangi; Khatri, Anand; Dumbre, P.B. (International Journal of Recent Advances in Engineering and Technology, 2025)
Efficient livestock management is crucial for modern agriculture, yet traditional methods remain labor-intensive and error-prone. This research presents an optimized AI-powered real time livestock monitoring system leveraging the YOLOv9 object detection algorithm. The proposed system enhances livestock detection and counting by incorporating advanced model optimization techniques, real-time anomaly detection, and scalable deployment on embedded systems like Raspberry Pi. By addressing challenges such as occlusions, variable lighting conditions, and dynamic animal movement, this system provides an accurate, cost-effective solution for farmers. Additionally, integration with IoT sensors enables health monitoring and behavioral analysis, facilitating data-driven decision-making. Experimental results demonstrate high accuracy, low latency, and improved scalability, highlighting the system's potential to revolutionize precision livestock management.