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@article{178517,
        author = {G. Kokila and S. Balamoorthy and R. Jeevakumar and N. Rajamani and S. Thanveer},
        title = {IMPROVING PLANT DISEASE CLASSIFICATION WITH DEEP-LEARNING-BASED PREDICTION MODEL USING EXPLAINABLE ARTIFICIAL INTELLIGENCE},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4197-4199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178517},
        abstract = {Crop diseases significantly hinder agricultural productivity, often resulting in economic losses and heightened concerns over food availability. Early and accurate identification of these diseases is e ssential for effective intervention and improved crop health. This study presents a user-friendly Android application that employs Convolutional Neural Networks (CNNs) to detect and classify diseases in the leaves of tomato, potato, and corn plants. By utilizing a trained image dataset, the system delivers real-time predictions, including the disease name, visible symptoms, and suggested treatments. The application is built using TensorFlow Lite, enabling it to operate offline and provide immediate results, thereby supporting farmers in timely disease management and yield improvement.},
        keywords = {plant pathology, deep learning, mobile application, TensorFlow Lite, CNN, disease detection},
        month = {May},
        }