International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Plant Disease Detection Using Convolutional Neural Network

Author(s) Saranathan U, Shenbagavadivu S, Sarath S, Vishal Kabilan K, Praveen Kumar B
Country India
Abstract Plant disease detection is critical in agriculture, significantly impacting crop yield and food supply. This paper introduces an innovative approach utilizing deep learning and computer vision techniques to detect and classify plant diseases. The proposed method involves image processing to analyze leaf health conditions using a dataset of varied leaf images exhibiting disease manifestations. The model is trained to recognize distinct patterns associated with different diseases, enabling early detection and intervention. This AI-based system aims to enhance agricultural productivity, minimize crop losses, and contribute to food security. Furthermore, it has potential application in mobile platforms, providing farmers with a user-friendly tool for managing plant diseases. Nonetheless, challenges such as image condition variability and the necessity for comprehensive, diverse datasets for model training require further research and development.
Keywords CNN Algorithm, Keras, Tensorflow, Dataset, Plant Disease and etc.
Field Engineering
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-09
Cite This Plant Disease Detection Using Convolutional Neural Network - Saranathan U, Shenbagavadivu S, Sarath S, Vishal Kabilan K, Praveen Kumar B - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19536
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.19536
Short DOI https://doi.org/gttvgf
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