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@article{178032,
        author = {N.pranathi and M.varsha and P. nandhitha roopini and S.swathi},
        title = {PLANT LEAF DISEASES DETECTION USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2212-2216},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178032},
        abstract = {World tomato yields are being threatened by climate change and disease, endangering food security and economic stability. Deep transfer learning through convolutional neural networks(CNNs) is employed in this research to differentiate nine wide spread tomato leaf diseases and healthy leaves using raw images alone without preprocessing. Through the use of pretrained models, the system performs well on a variety of evaluation metrics. The 10-repeated experiments to make them robust provided me and scores of 99.3% precision,99.2% F1 score,99.1% recall, and 99.4%accuracy.The result prove the efficiency of CNNs in the detection of diseases in agriculture and justify the construction of affordable, AI-powered tools for real-time diagnosis. The applications can help farmers and pathologists immensely in early disease control, especially in resource -poor settings, and thus minimize crop loss and enhance productivity. This work demonstrates the real potential of AI in sustainable agriculture.},
        keywords = {deep learning; tomatoes; virus; bacteria; blight; spot; mold; image classification; artificial intelligence.},
        month = {May},
        }