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Erschienen in:
2023 | OriginalPaper | Buchkapitel
A Novel Approach to Detect Plant Disease Using DenseNet-121 Neural Network
verfasst von : Nilesh Dubey, Esha Bhagat, Swapnil Rana, Kirtan Pathak
Erschienen in: Smart Trends in Computing and Communications
Verlag: Springer Nature Singapore
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Abstract
The disease of crops is a major risk to food security and can incur a makeable loss to the people. But, the latest development in deep learning for solving this problem surpasses all the traditional methods in terms of efficiency, time period for detection and accuracy. In this paper, we came up with a rapid identification of leaf image and classify the image to correct class by using classical deep neural network architecture, DenseNet-121. This deep learning model has the ability to recognize 15 types of different plant disease, three of which are healthy ones, for better accurate results. The algorithm is highly optimized to produce results in less than 5 s after being fed into the system. The model’s total testing accuracy for plant disease detection is 99%.