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@article{188659,
        author = {Ahsa Ifra W and Hema M S and Krutika K and Prof. Mallinath Swamy},
        title = {Pashunetra: Intelligent Image-Based System for Smart Livestock Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2708-2711},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188659},
        abstract = {Accurate identification of cattle and buffaloes is vital for maintaining comprehensive records, ensuring traceability, and maximizing productivity in Indian livestock management. Traditional, manual identification methods such as ear tagging are invasive, prone to human error, and suffer from tag loss or damage. This project introduces Pashunetra—an AI-powered, non-invasive image-based classification system using deep learning and computer vision to automate livestock identification based on unique body features. The dataset comprises approximately 800 images across three cattle breeds, rigorously collected and augmented to improve model generalization. The system leverages the efficiency of transfer learning, deploying MobileNetV2 and Efficient Net architectures. These lightweight models were selected to balance high classification accuracy with low computational overhead, making the solution ideal for real-time edge deployment. Experimental results, detailed in TABLE I, demonstrate reliable accuracy and superior performance from Efficient Net across all key classification metrics, validating Pashunetra as a highly viable alternative for smart livestock management decisions.},
        keywords = {AI-powered system, Breed classification, Computer vision, Deep learning, Efficient Net, Livestock identification, MobileNetV2, Pashunetra, Transfer learning.},
        month = {December},
        }