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@article{177686,
        author = {HARIHARAN K and ESSAKKIYUVABALAJI J and HARI T},
        title = {Deep Learning-Based Plant Disease Detection Using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2710-2714},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177686},
        abstract = {In order to identify whether leaf photos are healthy or diseased, this study proposes a Plant Disease Detection System that uses Deep Learning, more especially Convolutional Neural Networks (CNNs). The PlantVillage dataset is used to train the model, and preprocessing methods like data augmentation, picture scaling, and normalization are used to improve performance. The CNN architecture, which is implemented with TensorFlow and Keras, consists of convolutional, pooling, and dense layers for effective feature extraction and classification. The technology provides a workable method to provide improved crop monitoring and management by achieving high accuracy in image-based disease prediction.},
        keywords = {Agricultural AI, image processing, convolutional neural networks (CNN), deep learning, image classification, plant village dataset, leaf image analysis, data augmentation, precision agriculture, TensorFlow, Keras, crop health monitoring, smart farming, early disease diagnosis, and plant disease detection.},
        month = {May},
        }