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@article{190698,
        author = {Aarti Basavantappa Valsang and Aishwarya Keshi},
        title = {Image based breed recognition for cattle and buffaloes Using AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1790-1795},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190698},
        abstract = {India is home to a diverse population of indigenous cattle and buffalo breeds that contribute significantly to the nation’s dairy and agricultural economy. Accurate breed identification is essential for genetic improvement, traceability, livestock insurance, and scientific documentation; however, traditional visual recognition methods are subjective and require expert knowledge. This paper presents an AI-based image recognition framework for automated classification of major Indian cattle and buffalo breeds using deep learning techniques. The proposed system employs transfer learning on convolution neural network (CNN) and vision transformer (ViT) architectures, combined with data augmentation, to handle variations in pose, illumination, background, and age. A curated dataset consisting of multi-angle images of breeds such as Gir, Sahiwal, Red Sindhi, Kankrej, Murrah, Jaffarabadi, and Mehsana was used for training and evaluation. Experimental results demonstrate high classification accuracy across visually similar breeds, outperforming traditional machine-learning approaches. The system can be integrated into mobile or web platforms for field-level breed identification, supporting digital livestock management and precision dairy farming in India. This work highlights the potential of computer vision as a scalable tool for improving livestock documentation and genetic resource conservation.},
        keywords = {Computer vision, cattle breed identification, buffalo breed recognition, deep learning, transfer learning, precision livestock farming, Indian indigenous breeds.},
        month = {January},
        }