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@article{176408,
        author = {SAI KIRAN B and ASHRAF MOHD and SHRAVAN KUMAR E},
        title = {AI-POWERED POTATO LEAF DISEASE IDENTIFICATION AND CLASSIFICATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7119-7126},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176408},
        abstract = {This paper proposes a smart and adaptive plant disease detection system for real-time identification and classification of leaf infections in agriculture. Utilizing convolutional neural networks (CNNs) for visual diagnosis, the system classifies potato leaf conditions as healthy, early blight, late blight, or other infections with 90% precision. Integrated with an Android application, the solution enables farmers to upload leaf images for instant analysis through a cloud-based inference model. The system performs image enhancement, segmentation, and multi-disease classification for accurate results. Coupled with advanced preprocessing techniques, the model is trained on a large dataset of labeled leaf images to ensure robust detection. The system supports early response strategies by providing instant feedback, improving productivity and minimizing crop loss. Designed for real-world agricultural environments, this solution offers an accessible, efficient, and automated approach to plant health monitoring and disease management.},
        keywords = {Early blight, Convolutional Neural Network, potato leaf diseases, Machine learning, Weather parameters, Sustainable agriculture.},
        month = {April},
        }