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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2022.890051/full |
| _version_ | 1811243427404185600 |
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| author | Helong Yu Zhibo Men Chunguang Bi Huanjun Liu |
| author_facet | Helong Yu Zhibo Men Chunguang Bi Huanjun Liu |
| author_sort | Helong Yu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T14:08:24Z |
| format | Article |
| id | doaj.art-143998d50ad04f3ea2126f599267e6eb |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T14:08:24Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-143998d50ad04f3ea2126f599267e6eb2022-12-22T03:30:01ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2022-06-011310.3389/fpls.2022.890051890051Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention MechanismHelong Yu0Zhibo Men1Chunguang Bi2Huanjun Liu3College of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, ChinaAiming 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.https://www.frontiersin.org/articles/10.3389/fpls.2022.890051/fullsemantic segmentationweed recognitionfeature fusionchannel attention mechanismimproved UNet model |
| spellingShingle | Helong Yu Zhibo Men Chunguang Bi Huanjun Liu Research on Field Soybean Weed Identification Based on an Improved UNet Model Combined With a Channel Attention Mechanism Frontiers in Plant Science semantic segmentation weed recognition feature fusion channel attention mechanism improved UNet model |
| 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 | semantic segmentation weed recognition feature fusion channel attention mechanism improved UNet model |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2022.890051/full |
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