IMAGE BASED BOVINE BREED RECOGNITION SYSTEM

The-Image-Based Bovine Breed Recognition System is an AI-driven approach designed for the automatic identification of Indian cattle and buffalo breeds using deep learning and computer vision. The project employs a Convolutional Neural Network (CNN) to recognize breed- specific visual traits such as skin texture, horn shape, and facial patterns from 2D images. A curated dataset of multiple indigenous bovine breeds was pre-processed through normalization, augmentation, and noise reduction to improve the models robustness and accuracy under varied environmental conditions. The CNN model architecture integrates multiple convolutional and pooling layers for hierarchical feature extraction, followed by dense and soft max layers for multi- class classification. The model was trained using the Adam optimizer with categorical cross-entropy loss and evaluated using performance metrics like accuracy, precision, recall, and F1-score. The proposed framework achieved high accuracy in distinguishing visually similar breeds, showcasing its reliability and adaptability for real-world agricultural applications. A Flask-based web interface, connected to a cloud database, enables users to upload animal images and instantly obtain breed predictions with confidence scores. This system empowers farmers, veterinarians, and researchers to identify breeds efficiently without expert supervision, supporting the goals of Precision Livestock Farming (PLF) and national programs such as the Rashtriya Gokul Mission. By integrating deep learning and image analytics, the project contributes to the digital transformation of livestock management, promoting sustainable and data-driven practices within Indias agricultural ecosystem.