Cargando…
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...
Autores principales: | , , , , |
---|---|
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 |
_version_ | 1784821461321515008 |
---|---|
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%. |
format | Online Article Text |
id | pubmed-9621083 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT luyang imageclassificationandidentificationforriceleafdiseasesbasedonimprovedwoacwsimplenet AT zhangxinmeng imageclassificationandidentificationforriceleafdiseasesbasedonimprovedwoacwsimplenet AT zengnianyin imageclassificationandidentificationforriceleafdiseasesbasedonimprovedwoacwsimplenet AT liuwanting imageclassificationandidentificationforriceleafdiseasesbasedonimprovedwoacwsimplenet AT shangrou imageclassificationandidentificationforriceleafdiseasesbasedonimprovedwoacwsimplenet |