Advancing AI-Powered Real-Time Livestock Management: An Optimized YOLOv9-Based Approach

Abstract

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.