AI-Powered Mobile Solution For Livestock Management for Intelligent Cattle Breed Classification System
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Abstract
This paper presents CattleSense, an intelligent mobile application for automatic classification of Indian cattle and buffalo breeds that combines computer vision and deep learning to address the challenges of manual breed identification in rural livestock management. The system integrates YOLOV12 for object detection with a custom-trained CNN classifier, supported by OpenCV preprocessing, ReactJS frontend, and FastAPI backend. Experiments conducted under real field conditions across 15 farms in Gujarat, Punjab, and Rajasthan achieved 90.5% classification accuracy while maintaining sub-2-second inference time on mid-range mobile devices. The offline-first architecture ensures complete functionality in areas with limited connectivity. Feature analysis highlighted the importance of morphological traits such as hump shape, coat patterns, and regional metadata. The proposed framework demonstrates excellent practicality for small and marginal farmers, significantly reducing manual inspection time from 30-45 minutes to under 2 minutes per animal while preserving data privacy through on-device processing. This study con-tributes to smart agriculture by delivering a farmer-centric, scalable solution that effectively leverages modern Al techniques for real-world livestock management
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