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Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to...

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Autores principales: Lu, Yang, Zhang, Xinmeng, Zeng, Nianyin, Liu, Wanting, Shang, Rou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9621083/
https://www.ncbi.nlm.nih.gov/pubmed/36325573
http://dx.doi.org/10.3389/fpls.2022.1008819
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author Lu, Yang
Zhang, Xinmeng
Zeng, Nianyin
Liu, Wanting
Shang, Rou
author_facet Lu, Yang
Zhang, Xinmeng
Zeng, Nianyin
Liu, Wanting
Shang, Rou
author_sort Lu, Yang
collection PubMed
description In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.
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spelling pubmed-96210832022-11-01 Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet Lu, Yang Zhang, Xinmeng Zeng, Nianyin Liu, Wanting Shang, Rou Front Plant Sci Plant Science In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%. Frontiers Media S.A. 2022-10-17 /pmc/articles/PMC9621083/ /pubmed/36325573 http://dx.doi.org/10.3389/fpls.2022.1008819 Text en Copyright © 2022 Lu, Zhang, Zeng, Liu and Shang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Lu, Yang
Zhang, Xinmeng
Zeng, Nianyin
Liu, Wanting
Shang, Rou
Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet
title Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet
title_full Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet
title_fullStr Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet
title_full_unstemmed Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet
title_short Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet
title_sort image classification and identification for rice leaf diseases based on improved woacw_simplenet
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9621083/
https://www.ncbi.nlm.nih.gov/pubmed/36325573
http://dx.doi.org/10.3389/fpls.2022.1008819
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