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@article{173883,
        author = {Ms. Divya Verma},
        title = {Deep learning approaches for potato leaf disease detection and categarization: A Review},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1835-1842},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173883},
        abstract = {The potato (Solanum tuberosum) is one of the most important crops in the world, and sometimes productivity can get affected by a diversity of diseases, with leaf disease being one of them. The early detection and accurate diagnosis of these diseases would help in reducing their spread and thus minimize crop loss. While many approaches have been established for diagnosing and classifying the diseases of potato leaves, the demand for more accurate and timely detection is noteworthy. In this paper, we analyze and review the various methods deployed in the detection of diseases affecting potato leaves. We also propose an improved model for detection that will be covered in our futurist research.},
        keywords = {Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Deep Learning (DL), Genetic Algorithm (GA), K-Nearest Neighbor (KNN), Machine Learning (ML), Potato Leaf Disease Detection, Convolutional Neural Networks (CNNs), Support Vector Machines (SVM).},
        month = {March},
        }