Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism
Northern corn leaf blight (NCLB) is caused by a fungus and can be susceptible to the disease throughout the growing period of corn, posing a significant impact on corn yield. Aiming at the problems of under-segmentation, over-segmentation, and low segmentation accuracy in the traditional segmentatio...
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MDPI AG
2024-11-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/11/2652 |
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| author | Ye Mu Ke Li Yu Sun Yu Bao |
| author_facet | Ye Mu Ke Li Yu Sun Yu Bao |
| author_sort | Ye Mu |
| collection | DOAJ |
| description | Northern corn leaf blight (NCLB) is caused by a fungus and can be susceptible to the disease throughout the growing period of corn, posing a significant impact on corn yield. Aiming at the problems of under-segmentation, over-segmentation, and low segmentation accuracy in the traditional segmentation model of northern corn leaf blight, this study proposes a segmentation method based on an improved U-Net network model. By introducing a convolutional layer and maximum pooling layer to a VGG19 network, the channel attention module and spatial attention module (CBAM) are fused, and the squeeze excitation (SE) attention mechanism is combined. This enhances image feature decoding, integrates feature maps of each layer, strengthens the feature extraction process, expands the sensory fields and aggregates context information, and reduces the loss of location and dense semantic information caused by the pooling operation. Findings from the study show that the proposed NCLB-Net has significantly improved the MIoU and PA indexes, reaching 92.43% and 94.71%, respectively. Compared with the traditional methods, U-Net, SETR, DAnet, OCnet, PSPNet, etc., the MIoU is improved by 20.81%, 16.10%, 9.79%, 5.27%, and 11.06%, and the PA is improved by 11.49%, 8.18%, 9.54%, 13.11%, and 6.26%, respectively. |
| format | Article |
| id | doaj-art-82d6f38b80b7450da3a7001debfad0ee |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-82d6f38b80b7450da3a7001debfad0ee2025-08-20T02:26:51ZengMDPI AGAgronomy2073-43952024-11-011411265210.3390/agronomy14112652Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention MechanismYe Mu0Ke Li1Yu Sun2Yu Bao3College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Life Science, Changchun Normal University, Changchun 130118, ChinaNorthern corn leaf blight (NCLB) is caused by a fungus and can be susceptible to the disease throughout the growing period of corn, posing a significant impact on corn yield. Aiming at the problems of under-segmentation, over-segmentation, and low segmentation accuracy in the traditional segmentation model of northern corn leaf blight, this study proposes a segmentation method based on an improved U-Net network model. By introducing a convolutional layer and maximum pooling layer to a VGG19 network, the channel attention module and spatial attention module (CBAM) are fused, and the squeeze excitation (SE) attention mechanism is combined. This enhances image feature decoding, integrates feature maps of each layer, strengthens the feature extraction process, expands the sensory fields and aggregates context information, and reduces the loss of location and dense semantic information caused by the pooling operation. Findings from the study show that the proposed NCLB-Net has significantly improved the MIoU and PA indexes, reaching 92.43% and 94.71%, respectively. Compared with the traditional methods, U-Net, SETR, DAnet, OCnet, PSPNet, etc., the MIoU is improved by 20.81%, 16.10%, 9.79%, 5.27%, and 11.06%, and the PA is improved by 11.49%, 8.18%, 9.54%, 13.11%, and 6.26%, respectively.https://www.mdpi.com/2073-4395/14/11/2652northern corn leaf blightsegmentationattention mechanismU-Net networkimage segmentationmaize disease |
| spellingShingle | Ye Mu Ke Li Yu Sun Yu Bao Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism Agronomy northern corn leaf blight segmentation attention mechanism U-Net network image segmentation maize disease |
| title | Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism |
| title_full | Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism |
| title_fullStr | Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism |
| title_full_unstemmed | Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism |
| title_short | Semantic Segmentation of Corn Leaf Blotch Disease Images Based on U-Net Integrated with RFB Structure and Dual Attention Mechanism |
| title_sort | semantic segmentation of corn leaf blotch disease images based on u net integrated with rfb structure and dual attention mechanism |
| topic | northern corn leaf blight segmentation attention mechanism U-Net network image segmentation maize disease |
| url | https://www.mdpi.com/2073-4395/14/11/2652 |
| work_keys_str_mv | AT yemu semanticsegmentationofcornleafblotchdiseaseimagesbasedonunetintegratedwithrfbstructureanddualattentionmechanism AT keli semanticsegmentationofcornleafblotchdiseaseimagesbasedonunetintegratedwithrfbstructureanddualattentionmechanism AT yusun semanticsegmentationofcornleafblotchdiseaseimagesbasedonunetintegratedwithrfbstructureanddualattentionmechanism AT yubao semanticsegmentationofcornleafblotchdiseaseimagesbasedonunetintegratedwithrfbstructureanddualattentionmechanism |