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Rice pest identification based on multi-scale double-branch GAN-ResNet

Rice production is crucial to the food security of all human beings, and how rice pests and diseases can be effectively prevented in and timely detected is a hotspot issue in the field of smart agriculture. Deep learning has become the preferred method for rice pest identification due to its excelle...

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Autores principales: Hu, Kui, Liu, YongMin, Nie, Jiawei, Zheng, Xinying, Zhang, Wei, Liu, Yuan, Xie, TianQiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140523/
https://www.ncbi.nlm.nih.gov/pubmed/37123817
http://dx.doi.org/10.3389/fpls.2023.1167121
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author Hu, Kui
Liu, YongMin
Nie, Jiawei
Zheng, Xinying
Zhang, Wei
Liu, Yuan
Xie, TianQiang
author_facet Hu, Kui
Liu, YongMin
Nie, Jiawei
Zheng, Xinying
Zhang, Wei
Liu, Yuan
Xie, TianQiang
author_sort Hu, Kui
collection PubMed
description Rice production is crucial to the food security of all human beings, and how rice pests and diseases can be effectively prevented in and timely detected is a hotspot issue in the field of smart agriculture. Deep learning has become the preferred method for rice pest identification due to its excellent performance, especially in the aspect of autonomous learning of image features. However, in the natural environment, the dataset is too small and vulnerable to the complex background, which easily leads to problems such as overfitting, and too difficult to extract the fine features during the process of training. To solve the above problems, a Multi-Scale Dual-branch structural rice pest identification model based on a generative adversarial network and improved ResNet was proposed. Based on the ResNet model, the ConvNeXt residual block was introduced to optimize the calculation ratio of the residual blocks, and the double-branch structure was constructed to extract disease features of different sizes in the input disease images, which it adjusts the size of the convolution kernel of each branch. In the complex natural environment, data pre-processing methods such as random brightness and motion blur, and data enhancement methods such as mirroring, cropping, and scaling were used to allow the dataset of 5,932 rice disease images captured from the natural environment to be expanded to 20,000 by the dataset in this paper. The new model was trained on the new dataset to identify four common rice diseases. The experimental results showed that the recognition accuracy of the new rice pest recognition model, which was proposed for the first time, improved by 2.66% compared with the original ResNet model. Under the same experimental conditions, the new model had the best performance when compared with classical networks such as AlexNet, VGG, DenseNet, ResNet, and Transformer, and its recognition accuracy could be as high as 99.34%. The model has good generalization ability and excellent robustness, which solves the current problems in rice pest identification, such as the data set is too small and easy to lead to overfitting, and the picture background is difficult to extract disease features, and greatly improves the recognition accuracy of the model by using a multi-scale double branch structure. It provides a superior solution for crop pest and disease identification.
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spelling pubmed-101405232023-04-29 Rice pest identification based on multi-scale double-branch GAN-ResNet Hu, Kui Liu, YongMin Nie, Jiawei Zheng, Xinying Zhang, Wei Liu, Yuan Xie, TianQiang Front Plant Sci Plant Science Rice production is crucial to the food security of all human beings, and how rice pests and diseases can be effectively prevented in and timely detected is a hotspot issue in the field of smart agriculture. Deep learning has become the preferred method for rice pest identification due to its excellent performance, especially in the aspect of autonomous learning of image features. However, in the natural environment, the dataset is too small and vulnerable to the complex background, which easily leads to problems such as overfitting, and too difficult to extract the fine features during the process of training. To solve the above problems, a Multi-Scale Dual-branch structural rice pest identification model based on a generative adversarial network and improved ResNet was proposed. Based on the ResNet model, the ConvNeXt residual block was introduced to optimize the calculation ratio of the residual blocks, and the double-branch structure was constructed to extract disease features of different sizes in the input disease images, which it adjusts the size of the convolution kernel of each branch. In the complex natural environment, data pre-processing methods such as random brightness and motion blur, and data enhancement methods such as mirroring, cropping, and scaling were used to allow the dataset of 5,932 rice disease images captured from the natural environment to be expanded to 20,000 by the dataset in this paper. The new model was trained on the new dataset to identify four common rice diseases. The experimental results showed that the recognition accuracy of the new rice pest recognition model, which was proposed for the first time, improved by 2.66% compared with the original ResNet model. Under the same experimental conditions, the new model had the best performance when compared with classical networks such as AlexNet, VGG, DenseNet, ResNet, and Transformer, and its recognition accuracy could be as high as 99.34%. The model has good generalization ability and excellent robustness, which solves the current problems in rice pest identification, such as the data set is too small and easy to lead to overfitting, and the picture background is difficult to extract disease features, and greatly improves the recognition accuracy of the model by using a multi-scale double branch structure. It provides a superior solution for crop pest and disease identification. Frontiers Media S.A. 2023-04-14 /pmc/articles/PMC10140523/ /pubmed/37123817 http://dx.doi.org/10.3389/fpls.2023.1167121 Text en Copyright © 2023 Hu, Liu, Nie, Zheng, Zhang, Liu and Xie 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
Hu, Kui
Liu, YongMin
Nie, Jiawei
Zheng, Xinying
Zhang, Wei
Liu, Yuan
Xie, TianQiang
Rice pest identification based on multi-scale double-branch GAN-ResNet
title Rice pest identification based on multi-scale double-branch GAN-ResNet
title_full Rice pest identification based on multi-scale double-branch GAN-ResNet
title_fullStr Rice pest identification based on multi-scale double-branch GAN-ResNet
title_full_unstemmed Rice pest identification based on multi-scale double-branch GAN-ResNet
title_short Rice pest identification based on multi-scale double-branch GAN-ResNet
title_sort rice pest identification based on multi-scale double-branch gan-resnet
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10140523/
https://www.ncbi.nlm.nih.gov/pubmed/37123817
http://dx.doi.org/10.3389/fpls.2023.1167121
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