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Our project presents an innovative approach to address the crucial issue of plant disease identification through the utilization of deep learning techniques. Agricultural productivity is significantly affected by plant diseases, leading to economic losses and food security concerns. In this research, a comprehensive dataset comprising images of healthy and diseased plants is collected and pre-processed for training deep convolutional neural networks (CNNs). The proposed system harnesses the power of deep learning to automatically learn intricate patterns and features from plant images, enabling accurate classification between healthy and diseased states. The trained model is evaluated on an independent dataset to assess its classification performance. Various deep learning architectures, such as VGG, ResNet, and Inception, are experimented with to identify the most suitable architecture for achieving high accuracy and robustness. To enhance the model's generalization capability, data augmentation techniques are employed during training. Transfer learning is also explored, allowing the pre-trained models to be fine-tuned for the specific task of plant disease classification. The performance metrics, including accuracy, precision, recall, and F1-score, are thoroughly evaluated to quantify the model's effectiveness. The proposed deep learning-based plant disease classification system holds great promise for real-world agricultural applications. Its automated and accurate nature has the potential to revolutionize plant disease management by enabling early detection and timely intervention. As a result, this research contributes to the advancement of precision agriculture practices, helping to mitigate the adverse impacts of plant diseases and promote sustainable agricultural production.
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Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: D.siva sankar Reddy, d.sivasankarareddy@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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