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@article{180249,
        author = {Gauri Walunj and Thorat Neha and Hatkar Neha and Shinde Durga},
        title = {Potato Plant Health Detector},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1632-1639},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180249},
        abstract = {The Potato plant diseases are one of the major problems in agriculture, which results in a decrease in crop yield and quality. This review covers recent advances in potato disease detection techniques based on imaging techniques and deep learning models, The paper brings to attention the accuracies obtained through hyperspectral imaging, thermal imaging, RGB imaging, and CNN-based deep learning models considering their efficiencies and real-world applicability. Discussionsarealsoheld with regard to challenges and future directions for the task of potato disease detection, including issues around data scarcity, model generalization, and real-time deployment},
        keywords = {CNN algorithm, Image Processing, Potato Leaf Disease.},
        month = {June},
        }