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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-8899009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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