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@article{185603,
        author = {Panchagnula Deepthi and Dr.Sadish Sendhil Murugaraj},
        title = {AN INNOVATIVE FRAMEWORK TO PREDICT TOMATO PLANT LEAF DIAGNOSIS USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2170-2176},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185603},
        abstract = {The article defines a new framework for precision agriculture to automate the diagnosis of decline of tomato plant leaves using deep learning technologies. A public tomato leaf dataset available on Kaggle was used to train and test CNN-based models, ResNet50 and EfficientNet, to detect tomato plant diseases. In addition, data augmentation and transfer learning were used to enhance the reliability and accuracy of the framework. The models provided remarkable classification performance on accuracy, precision, recall, and F1-scores indicating that the framework could be utilized in early disease detection, and sustainable crop management. The framework can also be potentially utilized in food security and in preventing losses in crops.},
        keywords = {Precision Agriculture, Deep Learning, Image Classification, Sustainable Agriculture, Food Security, Tomato Plant Leaves, Disease Detection.},
        month = {October},
        }