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Published Paper Details:
Depthwise Separable Convolution architectures for the identification of leaf diseases in Tomato Crop
Authors
D.Sandhya Rani,K.Shyamala
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Keywords
Plant diseases, Deep learning, point wise convolution, convolutional neural network, depth wise convolution, classification
Abstract
The early prevention of crop diseases and identifyingthem at early stage is very important for crop production. The usage of fertilizers and chemicals is causing a negative effect on the agriculture eco-system and causes economic loss. A reliable method to identify and diagnose the diseases will benefit the farmers. In this paper, a new model is introduced with depthwise separable convolution architecture for tomato plant disease detection using leaf images. The models were trained and tested on a subset of publicly available plant village dataste containing six different classes of healthy and diseased leaves. These depthwise separable convolutions achieved high gain in convergence speed and the accuracy is also improved. A novel model called CNNLDD is proposed and it is compared with reduced CNNLDD model and this reduced model achieved a classification accuracy of 98.39% with fewer parameters. The satisfactory accuracy and small size of this model makes it suitable for real time crop diagnosis with less computation cost and the training time is also minimized. The standard CNN model requires a huge number of parameters, computation cost is high and also the training time is more. Standard convolution is replaced with the depth wise separable convolution in order to minimize the number of parameters and computation cost.
IJCRT's Publication Details
Unique Identification Number - IJCRT2309486
Paper ID - 244305
Author type - Indian Author
Page Number(s) - e40-e47
Pubished in - Volume 11 | Issue 9 | September 2023
DOI (Digital Object Identifier) -
No Of Downloads - 146
Author Country - India, 508001, Nalgonda, Nalgonda, 508001, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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D.Sandhya Rani,K.Shyamala, "Depthwise Separable Convolution architectures for the identification of leaf diseases in Tomato Crop", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 9, pp.e40-e47, September 2023, Available at :http://www.ijcrt.org/papers/IJCRT2309486.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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