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Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism

Aiming at the problem that it is difficult to identify two types of weeds, grass weeds and broadleaf weeds, in complex field environments, this paper proposes a semantic segmentation method with an improved UNet structure and an embedded channel attention mechanism SE module. First, to eliminate the...

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Autores principales: Yu, Helong, Men, Zhibo, Bi, Chunguang, Liu, Huanjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240479/
https://www.ncbi.nlm.nih.gov/pubmed/35783959
http://dx.doi.org/10.3389/fpls.2022.890051
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author Yu, Helong
Men, Zhibo
Bi, Chunguang
Liu, Huanjun
author_facet Yu, Helong
Men, Zhibo
Bi, Chunguang
Liu, Huanjun
author_sort Yu, Helong
collection PubMed
description Aiming at the problem that it is difficult to identify two types of weeds, grass weeds and broadleaf weeds, in complex field environments, this paper proposes a semantic segmentation method with an improved UNet structure and an embedded channel attention mechanism SE module. First, to eliminate the semantic gap between low-dimensional semantic features and high-dimensional semantic features, the UNet model structure is modified according to the characteristics of different types of weeds, and the feature maps after the first five down sampling tasks are restored to the same original image through the deconvolution layer. Hence, the final feature map used for prediction is obtained by the fusion of the upsampling feature map and the feature maps containing more low-dimensional semantic information in the first five layers. In addition, ResNet34 is used as the backbone network, and the channel attention mechanism SE module is embedded to improve useful features. The channel weight is determined, noise is suppressed, soybean and grass weeds are identified, and broadleaf weeds are extracted through digital image morphological processing, and segmented images of soybean plants, grass weeds and broadleaf weeds are generated. Moreover, compared with the standard semantic segmentation models, FCN, UNet, and SegNet, the experimental results show that the overall performance of the model in this paper is the best. The average intersection ratio and average pixel recognition rate in a complex field environment are 0.9282 and 96.11%, respectively. On the basis of weed classification, the identified weeds are further refined into two types of weeds to provide technical support for intelligent precision variable weed spraying.
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spelling pubmed-92404792022-06-30 Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism Yu, Helong Men, Zhibo Bi, Chunguang Liu, Huanjun Front Plant Sci Plant Science Aiming at the problem that it is difficult to identify two types of weeds, grass weeds and broadleaf weeds, in complex field environments, this paper proposes a semantic segmentation method with an improved UNet structure and an embedded channel attention mechanism SE module. First, to eliminate the semantic gap between low-dimensional semantic features and high-dimensional semantic features, the UNet model structure is modified according to the characteristics of different types of weeds, and the feature maps after the first five down sampling tasks are restored to the same original image through the deconvolution layer. Hence, the final feature map used for prediction is obtained by the fusion of the upsampling feature map and the feature maps containing more low-dimensional semantic information in the first five layers. In addition, ResNet34 is used as the backbone network, and the channel attention mechanism SE module is embedded to improve useful features. The channel weight is determined, noise is suppressed, soybean and grass weeds are identified, and broadleaf weeds are extracted through digital image morphological processing, and segmented images of soybean plants, grass weeds and broadleaf weeds are generated. Moreover, compared with the standard semantic segmentation models, FCN, UNet, and SegNet, the experimental results show that the overall performance of the model in this paper is the best. The average intersection ratio and average pixel recognition rate in a complex field environment are 0.9282 and 96.11%, respectively. On the basis of weed classification, the identified weeds are further refined into two types of weeds to provide technical support for intelligent precision variable weed spraying. Frontiers Media S.A. 2022-06-15 /pmc/articles/PMC9240479/ /pubmed/35783959 http://dx.doi.org/10.3389/fpls.2022.890051 Text en Copyright © 2022 Yu, Men, Bi and Liu. 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
Yu, Helong
Men, Zhibo
Bi, Chunguang
Liu, Huanjun
Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism
title Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism
title_full Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism
title_fullStr Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism
title_full_unstemmed Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism
title_short Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism
title_sort research on field soybean weed identification based on an improved unet model combined with a channel attention mechanism
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240479/
https://www.ncbi.nlm.nih.gov/pubmed/35783959
http://dx.doi.org/10.3389/fpls.2022.890051
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