Sandeep Bariwa
Dr. Swapnil Singhal
Keywords:
Deep Learning Techniques, Plant Disease, Convolutional Neural Network, GoogleNet model.
Abstract:
In this study, we developed a plant disease detection system using imaging technology that autonomously identifies symptoms on the leaves and stems of plants, promoting the growth of healthy plants on the farm. The system tracks all alterations detected in plants and distinctive traits like leaves and stems, automatically recognizes the modifications, and alerts the user. This study offers an assessment of current plant disease detection systems. The most recent convolutional neural network (CNN) based on deep learning has significantly improved image classification precision. Motivated by CNN's achievements in image classification, this paper focuses on the pre-trained deep learning approach for identifying plant diseases. This work's contribution has two facets: the most sophisticated large-scale architectures, like AlexNet and GoogleNet. The pre-trained models of AlexNet and GoogleNet were developed and evaluated using datasets obtained from the Kaggle website. Training, testing, and experimental findings indicate that the suggested architecture can achieve a higher GoogleNet model with an accuracy of 99.10% in comparison to other models.
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International Journal of Recent Research and Review
ISSN: 2277-8322
Vol. XVII, Issue 4
December 2024
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PUBLISHED
December 2024
ISSUE
Vol. XVII, Issue 4
SECTION
Articles
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