Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net
Quickly and accurately identifying weed areas is of great significance for improving weeding efficiency, reducing pesticide residues, protecting soil ecological environment, and increasing crop yield and quality. Targeting low detection efficiency in complex agricultural environments and inability o...
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MDPI AG
2025-07-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/15/14/1471 |
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| author | Jinlong Wu Xin Wu Ronghui Miao |
| author_facet | Jinlong Wu Xin Wu Ronghui Miao |
| author_sort | Jinlong Wu |
| collection | DOAJ |
| description | Quickly and accurately identifying weed areas is of great significance for improving weeding efficiency, reducing pesticide residues, protecting soil ecological environment, and increasing crop yield and quality. Targeting low detection efficiency in complex agricultural environments and inability of multispectral input in weed recognition of minor grain based on unmanned aerial vehicles (UAVs), a semantic segmentation model for buckwheat weeds based on MSU-Net (multispectral U-shaped network) was proposed to explore the influence of different band optimizations on recognition accuracy. Five spectral features—red (R), blue (B), green (G), red edge (REdge), and near-infrared (NIR)—were collected in August when the weeds were more prominent. Based on the U-net image semantic segmentation model, the input module was improved to adaptively adjust the input bands. The neuron death caused by the original ReLU activation function may lead to misidentification, so it was replaced by the Swish function to improve the adaptability to complex inputs. Five single-band multispectral datasets and nine groups of multi-band combined data were, respectively, input into the improved MSU-Net model to verify the performance of our method. Experimental results show that in the single-band recognition results, the B band performs better than other bands, with mean pixel accuracy (mPA), mean intersection over union (mIoU), Dice, and F1 values of 0.75, 0.61, 0.87, and 0.80, respectively. In the multi-band recognition results, the R+G+B+NIR band performs better than other combined bands, with mPA, mIoU, Dice, and F1 values of 0.76, 0.65, 0.85, and 0.78, respectively. Compared with U-Net, DenseASPP, PSPNet, and DeepLabv3, our method achieved a preferable balance between model accuracy and resource consumption. These results indicate that our method can adapt to multispectral input bands and achieve good results in weed segmentation tasks. It can also provide reference for multispectral data analysis and semantic segmentation in the field of minor grain crops. |
| format | Article |
| id | doaj-art-af2c7feb961b43c68868c38306ef222c |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-af2c7feb961b43c68868c38306ef222c2025-08-20T03:36:13ZengMDPI AGAgriculture2077-04722025-07-011514147110.3390/agriculture15141471Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-NetJinlong Wu0Xin Wu1Ronghui Miao2College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030800, ChinaSchool of Comprehensive Health, Jinzhong College of Information, Jinzhong 030800, ChinaCollege of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030800, ChinaQuickly and accurately identifying weed areas is of great significance for improving weeding efficiency, reducing pesticide residues, protecting soil ecological environment, and increasing crop yield and quality. Targeting low detection efficiency in complex agricultural environments and inability of multispectral input in weed recognition of minor grain based on unmanned aerial vehicles (UAVs), a semantic segmentation model for buckwheat weeds based on MSU-Net (multispectral U-shaped network) was proposed to explore the influence of different band optimizations on recognition accuracy. Five spectral features—red (R), blue (B), green (G), red edge (REdge), and near-infrared (NIR)—were collected in August when the weeds were more prominent. Based on the U-net image semantic segmentation model, the input module was improved to adaptively adjust the input bands. The neuron death caused by the original ReLU activation function may lead to misidentification, so it was replaced by the Swish function to improve the adaptability to complex inputs. Five single-band multispectral datasets and nine groups of multi-band combined data were, respectively, input into the improved MSU-Net model to verify the performance of our method. Experimental results show that in the single-band recognition results, the B band performs better than other bands, with mean pixel accuracy (mPA), mean intersection over union (mIoU), Dice, and F1 values of 0.75, 0.61, 0.87, and 0.80, respectively. In the multi-band recognition results, the R+G+B+NIR band performs better than other combined bands, with mPA, mIoU, Dice, and F1 values of 0.76, 0.65, 0.85, and 0.78, respectively. Compared with U-Net, DenseASPP, PSPNet, and DeepLabv3, our method achieved a preferable balance between model accuracy and resource consumption. These results indicate that our method can adapt to multispectral input bands and achieve good results in weed segmentation tasks. It can also provide reference for multispectral data analysis and semantic segmentation in the field of minor grain crops.https://www.mdpi.com/2077-0472/15/14/1471UAVmultispectralbuckwheatweed identificationU-NetMSU-Net |
| spellingShingle | Jinlong Wu Xin Wu Ronghui Miao Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net Agriculture UAV multispectral buckwheat weed identification U-Net MSU-Net |
| title | Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net |
| title_full | Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net |
| title_fullStr | Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net |
| title_full_unstemmed | Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net |
| title_short | Research on Buckwheat Weed Recognition in Multispectral UAV Images Based on MSU-Net |
| title_sort | research on buckwheat weed recognition in multispectral uav images based on msu net |
| topic | UAV multispectral buckwheat weed identification U-Net MSU-Net |
| url | https://www.mdpi.com/2077-0472/15/14/1471 |
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