Image Based Breed Recognition for Cattle and Buffaloes of India

Abstract

India's agricultural economy and genetic biodiversity are deeply rooted in its vast population of indigenous cattle and buffalo breeds. Traditional methods for breed identification are manual, subjective, and require specialized expertise, creating significant inefficiencies in livestock management and conservation. This paper proposes a hybrid automated system for cattle breed identification using deep learning. Our methodology combines two powerful computer vision techniques: (1) A fine-tuned Convolutional Neural Network (CNN) for holistic classification based on overall phenotypical features, and (2) A YOLO (You Only Look Once) object detection model to perform fine-grained biometric analysis by specifically locating and identifying muzzle patterns. This dual-approach allows the system to cross-validate a broad classification with a highly specific biometric marker. Our proof-of-concept models, trained on public datasets, achieved high accuracy for both whole-body classification (97.44% mAP@0.5) and muzzle detection (99% mAP@0.5), demonstrating the viability and robustness of this hybrid strategy. This research provides a framework for a scalable and accessible tool to aid farmers, researchers, and policymakers in the preservation and management of India's valuable livestock resources.