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@article{176038,
        author = {Chetan Choudhari and Dr. Prakash Prasad and Khemant Borkar and Abhishek Shende and Aniket Randive and Saurabh Nagrare},
        title = {Plant Disease Prediction Using Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6845-6849},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176038},
        abstract = {In Agriculture plant disease detection is global and leading to crop losses and food security. Early accurate identifying of plant health is critical for effective disease prevention. In this project, we developed a deep learning-based model to automatically detect health of plant. The model uses CNN, including VGG16, ResNet, Inception, and a custom CNN architecture, to classify images into various disease categories.
We trained the models on the publicly available dataset, which contains thousands of labeled images representing different plant species and their corresponding diseases. Image augmentation, normalization, and preprocessing techniques (rotation, flipping, zooming) were applied to improve model performance and generalization. The model identified and evaluated based on their accuracy, precision, recall, F1-score, and confusion matrix.},
        keywords = {Plant disease, Feature extraction, Deep learning, Machine learning, Classification.},
        month = {April},
        }