International Journal of Science, Engineering and TechnologyAuthors: Vaishnavi Dattaray Nalawade, Professor Bangar Abhishek Popat, Professor Bhosale S.B.
Abstract: Reliable breed identification underpins conservation, breeding, registration and traceability in India’s cattle and buffalo sectors, yet it still depends largely on expert visual judgement. This review examines how computer vision and deep learning have been applied to image-based breed recognition of Indian cattle and buffaloes. A structured narrative search was carried out in October 2026, and 17 records were examined: studies on Indian breeds, adjacent non-Indian breed-classification and identification work, a public dataset, and an earlier systematic review of cattle identification. Reported accuracies for Indian cat-tle range from 72.5% for the visually similar Tharparkar and Hariana pair to about 95% for a lightweight CNN on four zebu breeds, while a YOLOv12 sys-tem covering 15 breeds reported 69.8% mAP at IoU 0.5. These figures cannot be compared directly because datasets, class definitions, data splits and met-rics differ. No peer-reviewed, breed-level study of Indian buffaloes was identi-fied, and the buffalo evidence is limited to a muzzle-based individual-identification study from Türkiye. Recurrent weaknesses include small and imbalanced datasets, unclear animal-level splitting, absent external validation, internal inconsistencies in reporting, and little attention to crossbreds, explain-ability and low-cost deployment. We synthesise these gaps and outline a con-ceptual, not yet validated, framework that combines image-quality control, multi-view fusion, uncertainty-aware output and expert verification.
