Image-Based Breed Recognition of Indian Cattle and Buffaloes Using YOLOv12 and Roboflow Cloud Platform

India's livestock sector faces significant challenges in accurate breed identification, with traditional manual methods achieving only 65-70% accuracy while consuming 40-60% of Field Level Workers' (FLWs) time. This paper presents BreedVision, an AI-powered web system for automated cattle and buffalo breed recognition using YOLOv12 object detection trained on 3,683 annotated images covering 15 major Indian breeds. The model achieves 69.8% mean Average Precision at IoU 0.5, with breed-specific accuracies reaching 87% for Bargur, 85% for Dangi, and 83% for Ongole and Alambadi. The system integrates ReactJS frontend with WebRTC camera capture, Python Flask backend with OpenCV preprocessing, and Roboflow cloud inference, providing real-time classification with confidence scoring and Excel export for government BPA system integration. Field testing demonstrates 40-60% reduction in FLW workload while improving data accuracy to 70% overall.