Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183916,
        author = {Ranjitha M and Yashaswini Y},
        title = {Plant Leaf Disease Detection Using CNN model Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3671-3673},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183916},
        abstract = {This research proposes an innovative (CNN) framework for automated identification of plant leaf diseases through advanced deep learning methodologies. The system utilizes image preprocessing techniques combined with multi-layered CNN architecture to classify various plant diseases with high accuracy. Implementation involves TensorFlow and Keras libraries for model development, processing datasets containing healthy and diseased leaf samples across multiple plant species. The framework incorporates data augmentation, transfer learning, and feature extraction mechanisms to enhance classification performance. Experimental results demonstrate superior accuracy rates of 94.7% for disease detection across tomato, potato, and corn crops. The proposed solution addresses agricultural challenges by providing rapid, cost-effective disease identification tools for farmers and agricultural specialists.},
        keywords = {Convolutional Neural Networks, Plant Disease Detection, Deep Learning, Image Classification, Agricultural Technology, Computer Vision},
        month = {August},
        }