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@article{174598,
        author = {Prathmesh Ghangare and Dr. P.D. Khandait and Pranay Zingre and Ashish Mahalle},
        title = {Apple Leaf Disease Detection and Classification System Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {97-103},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174598},
        abstract = {The agricultural sector plays a vital role in global food security and economic stability but faces challenges such as crop diseases that impact productivity and quality. Manual disease detection is labor-intensive and error-prone, especially in large fields, necessitating automated solutions. This study introduces a deep learning framework using Convolutional Neural Networks (CNNs) integrated with a Streamlit-based web interface for detecting cotton and apple leaf diseases. The methodology involves data collection, preprocessing through normalization and augmentation, model selection using DenseNet121, ResNet50V2, and Xception, and deployment via Streamlit. Transfer learning with pre-trained models enhances accuracy while optimizing training efficiency.},
        keywords = {Convolutional Neural Networks (CNNs), DenseNet121, Image Processing, InceptionV3, ResNet50V2, VGG16, VGG19.},
        month = {March},
        }