Precision Livestock Tracking: Advancements in Black Cattle Monitoring for Sustainable Agriculture

Abstract

Utilizing computer vision for animal behavior monitoring provides significant benefits by minimizing direct handling and capturing diverse traits through a single sensor. However, accurately identifying animals remains a challenge. To address this, this study introduces an innovative approach to monitor black cattle in dynamic agricultural environments to ensure their health welfare. By integrating advanced techniques like DETIC for automated labeling and YOLOv8 for real-time detection, the research emphasizes improving accuracy and robustness in tracking black cattle tracking within complex open ranch environments. Moreover, the customized ByteTrack model tailored for ranch scenarios significantly enhances cattle tracking across intricate landscapes. Achieving a mean Average Precision (mAP) of 0.901 and a Multi-Object Tracking Accuracy (MOTA) of average accuracy 92.185% of four videos, this approach appears to offer a viable resolution for conducting individual cattle behavior analysis experiments through the application of computer vision.