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@article{165136,
        author = {Rishabh Jain and Dr.Neeta Verma and Saksham Gupta and Rupansh Singh and Vaibhav Jaiswal},
        title = {PLANT LEAF DISEASE DETECTION USING CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {219-222},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165136},
        abstract = {For agricultural productivity to be high, plant leaf disease detection must be done effectively. In order to precisely identify diseases in tomato, apple, grape, corn, and potato plants, this research focuses on using Convolutional Neural Networks (CNNs). The suggested Deep CNN model performs better in disease classification when compared to VGG16 transfer learning. The model achieves high accuracy rates across various architectures by substituting depth-separable convolutions for standard convolutions, thereby reducing parameter count and computation cost. Promising results are shown by the MobileNetV2 architecture, which is especially well-suited for mobile devices. This work demonstrates how deep CNNs can be used to identify diseases more effectively in agricultural settings, highlighting their potential for use in real-time disease detection systems.},
        keywords = {Deep Learning, CNN, Plant Disease Detection, Convolutional Neural Network, Agriculture.},
        month = {},
        }