Sharief, A H and Malavika, Anumala and Sailaja, Matta and Vujwal, Mekala and Jahnavi, Kantubuktha and Chaitanya Vamsi, Yarraboina (2025) CNN-Based Plant Disease Diagnosis: A Step Towards Sustainable Farming. International Journal of Innovative Science and Research Technology, 10 (3): 25mar1972. pp. 2804-2811. ISSN 2456-2165
![IJISRT25MAR1972.pdf [thumbnail of IJISRT25MAR1972.pdf]](https://eprint.ijisrt.org/style/images/fileicons/text.png)
IJISRT25MAR1972.pdf - Published Version
Download (754kB)
Abstract
The Crop Disease Detection System is an innovative solution that addresses challenges in modern agriculture. With the growing global population and increasing pressure on food production, effective crop disease management is crucial. This system harnesses machine learning and image recognition to help farmers, gardeners, and agricultural professionals accurately diagnose plant diseases. By uploading images of affected crops, users can rely on advanced deep learning algorithms to identify specific diseases and receive tailored recommendations for mitigation. Using a CNN-based approach trained on the PlantVillage dataset with transfer learning, the system automates disease detection, reducing dependence on manual inspection. Designed for real-time deployment, it can be integrated into agricultural advisory platforms, offering scalable support across diverse crop types and environmental conditions.
Item Type: | Article |
---|---|
Subjects: | T Technology > T Technology (General) |
Divisions: | Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
Depositing User: | Editor IJISRT Publication |
Date Deposited: | 16 Apr 2025 11:33 |
Last Modified: | 16 Apr 2025 11:34 |
URI: | https://eprint.ijisrt.org/id/eprint/409 |