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Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism

Crop pests are a major agricultural problem worldwide because the severity and extent of their occurrence threaten crop yield. However, traditional pest image segmentation methods are limited, ineffective and time-consuming, which causes difficulty in their promotion and application. Deep learning m...

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Detalles Bibliográficos
Autores principales: Zhao, Shengyi, Liu, Jizhan, Bai, Zongchun, Hu, Chunhua, Jin, Yujie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8899009/
https://www.ncbi.nlm.nih.gov/pubmed/35265096
http://dx.doi.org/10.3389/fpls.2022.839572
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author Zhao, Shengyi
Liu, Jizhan
Bai, Zongchun
Hu, Chunhua
Jin, Yujie
author_facet Zhao, Shengyi
Liu, Jizhan
Bai, Zongchun
Hu, Chunhua
Jin, Yujie
author_sort Zhao, Shengyi
collection PubMed
description Crop pests are a major agricultural problem worldwide because the severity and extent of their occurrence threaten crop yield. However, traditional pest image segmentation methods are limited, ineffective and time-consuming, which causes difficulty in their promotion and application. Deep learning methods have become the main methods to address the technical challenges related to pest recognition. We propose an improved deep convolution neural network to better recognize crop pests in a real agricultural environment. The proposed network includes parallel attention mechanism module and residual blocks, and it has significant advantages in terms of accuracy and real-time performance compared with other models. Extensive comparative experiment results show that the proposed model achieves up to 98.17% accuracy for crop pest images. Moreover, the proposed method also achieves a better performance on the other public dataset. This study has the potential to be applied in real-world applications and further motivate research on pest recognition.
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spelling pubmed-88990092022-03-08 Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism Zhao, Shengyi Liu, Jizhan Bai, Zongchun Hu, Chunhua Jin, Yujie Front Plant Sci Plant Science Crop pests are a major agricultural problem worldwide because the severity and extent of their occurrence threaten crop yield. However, traditional pest image segmentation methods are limited, ineffective and time-consuming, which causes difficulty in their promotion and application. Deep learning methods have become the main methods to address the technical challenges related to pest recognition. We propose an improved deep convolution neural network to better recognize crop pests in a real agricultural environment. The proposed network includes parallel attention mechanism module and residual blocks, and it has significant advantages in terms of accuracy and real-time performance compared with other models. Extensive comparative experiment results show that the proposed model achieves up to 98.17% accuracy for crop pest images. Moreover, the proposed method also achieves a better performance on the other public dataset. This study has the potential to be applied in real-world applications and further motivate research on pest recognition. Frontiers Media S.A. 2022-02-21 /pmc/articles/PMC8899009/ /pubmed/35265096 http://dx.doi.org/10.3389/fpls.2022.839572 Text en Copyright © 2022 Zhao, Liu, Bai, Hu and Jin. 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
Zhao, Shengyi
Liu, Jizhan
Bai, Zongchun
Hu, Chunhua
Jin, Yujie
Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism
title Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism
title_full Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism
title_fullStr Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism
title_full_unstemmed Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism
title_short Crop Pest Recognition in Real Agricultural Environment Using Convolutional Neural Networks by a Parallel Attention Mechanism
title_sort crop pest recognition in real agricultural environment using convolutional neural networks by a parallel attention mechanism
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8899009/
https://www.ncbi.nlm.nih.gov/pubmed/35265096
http://dx.doi.org/10.3389/fpls.2022.839572
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