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Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network

We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease on tea leaves. The proposed DCNN consists of three bottleneck blocks, two pairs of convolutional (Conv) layers, and three dense layers. The bottleneck...

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Autores principales: Pandian, J. Arun, Nisha, Sam Nirmala, Kanchanadevi, K., Pandey, Abhay K., Rima, Samira Kabir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873464/
https://www.ncbi.nlm.nih.gov/pubmed/36703994
http://dx.doi.org/10.1155/2023/7876302
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author Pandian, J. Arun
Nisha, Sam Nirmala
Kanchanadevi, K.
Pandey, Abhay K.
Rima, Samira Kabir
author_facet Pandian, J. Arun
Nisha, Sam Nirmala
Kanchanadevi, K.
Pandey, Abhay K.
Rima, Samira Kabir
author_sort Pandian, J. Arun
collection PubMed
description We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease on tea leaves. The proposed DCNN consists of three bottleneck blocks, two pairs of convolutional (Conv) layers, and three dense layers. The bottleneck blocks contain depthwise, standard, and linear convolution layers. A single-lens reflex digital image camera was used to collect 1320 images of tea leaves from the North Bengal region of India for preparing the tea grey blight disease dataset. The nongrey blight diseased tea leaf images in the dataset were categorized into two subclasses, such as healthy and other diseased leaves. Image transformation techniques such as principal component analysis (PCA) color, random rotations, random shifts, random flips, resizing, and rescaling were used to generate augmented images of tea leaves. The augmentation techniques enhanced the dataset size from 1320 images to 5280 images. The proposed DCNN model was trained and validated on 5016 images of healthy, grey blight infected, and other diseased tea leaves. The classification performance of the proposed and existing state-of-the-art techniques were tested using 264 tea leaf images. Classification accuracy, precision, recall, F measure, and misclassification rates of the proposed DCNN are 98.99%, 98.51%, 98.48%, 98.49%, and 1.01%, respectively, on test data. The test results show that the proposed DCNN model performed superior to the existing techniques for tea grey blight disease detection.
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spelling pubmed-98734642023-01-25 Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network Pandian, J. Arun Nisha, Sam Nirmala Kanchanadevi, K. Pandey, Abhay K. Rima, Samira Kabir Comput Intell Neurosci Research Article We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease on tea leaves. The proposed DCNN consists of three bottleneck blocks, two pairs of convolutional (Conv) layers, and three dense layers. The bottleneck blocks contain depthwise, standard, and linear convolution layers. A single-lens reflex digital image camera was used to collect 1320 images of tea leaves from the North Bengal region of India for preparing the tea grey blight disease dataset. The nongrey blight diseased tea leaf images in the dataset were categorized into two subclasses, such as healthy and other diseased leaves. Image transformation techniques such as principal component analysis (PCA) color, random rotations, random shifts, random flips, resizing, and rescaling were used to generate augmented images of tea leaves. The augmentation techniques enhanced the dataset size from 1320 images to 5280 images. The proposed DCNN model was trained and validated on 5016 images of healthy, grey blight infected, and other diseased tea leaves. The classification performance of the proposed and existing state-of-the-art techniques were tested using 264 tea leaf images. Classification accuracy, precision, recall, F measure, and misclassification rates of the proposed DCNN are 98.99%, 98.51%, 98.48%, 98.49%, and 1.01%, respectively, on test data. The test results show that the proposed DCNN model performed superior to the existing techniques for tea grey blight disease detection. Hindawi 2023-01-17 /pmc/articles/PMC9873464/ /pubmed/36703994 http://dx.doi.org/10.1155/2023/7876302 Text en Copyright © 2023 J. Arun Pandian et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Pandian, J. Arun
Nisha, Sam Nirmala
Kanchanadevi, K.
Pandey, Abhay K.
Rima, Samira Kabir
Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network
title Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network
title_full Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network
title_fullStr Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network
title_full_unstemmed Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network
title_short Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network
title_sort grey blight disease detection on tea leaves using improved deep convolutional neural network
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873464/
https://www.ncbi.nlm.nih.gov/pubmed/36703994
http://dx.doi.org/10.1155/2023/7876302
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