SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images
Winter wheat lodging reduces wheat yield and poses a further risk to regional food security, emphasizing the need for timely and accurately monitoring of affected areas. Advances in Unmanned Aerial System (UAS) remote sensing and deep learning techniques provide new tools for detecting winter wheat...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525004617 |
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| author | Yahui Guo Wei Zhou Yongshuo H Fu Fanghua Hao Xuan Zhang Le Xu Ji Liu Yuhong He |
| author_facet | Yahui Guo Wei Zhou Yongshuo H Fu Fanghua Hao Xuan Zhang Le Xu Ji Liu Yuhong He |
| author_sort | Yahui Guo |
| collection | DOAJ |
| description | Winter wheat lodging reduces wheat yield and poses a further risk to regional food security, emphasizing the need for timely and accurately monitoring of affected areas. Advances in Unmanned Aerial System (UAS) remote sensing and deep learning techniques provide new tools for detecting winter wheat lodging. In this study, the RGB images of winter wheat were captured by the DJI Phantom 4 Pro V2.0 at flight heights of 60 m (0.8cm/pixel), 90 m (1.8cm/pixel), 120 m (3.1cm/pixel), and 150 m (4.3cm/pixel). A novel SegNeXt-RCMSCA network was proposed by integrating horizontal and vertical pooling with a multi-scale self-calibrated convolution function to enhance global contextual information. The SegNeXt-RCMSCA model achieved an Intersection over Union (IoU) of 86.72 %, F1 score of 92.89 %, Recall of 94.33 %, and Precision of 91.49 %. The model was tested using images with different spatial resolutions, and the results indicated that the 60 m (0.8cm/pixel) achieved the highest detection accuracy. The proposed SegNeXt-RCMSCA demonstrated strong potential for detecting lodging in other crops, offering a robust tool for improving the crop management in precision agriculture. By enabling timely and accurately lodging detection, the model facilitates crop damage assessment, harvest optimization, and informed field management, while supporting large-scale agricultural monitoring and intelligent decision-making in precision farming. |
| format | Article |
| id | doaj-art-83da28a7d223484ab82151c2a4053ddd |
| institution | DOAJ |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-83da28a7d223484ab82151c2a4053ddd2025-08-20T02:46:25ZengElsevierSmart Agricultural Technology2772-37552025-12-011210123010.1016/j.atech.2025.101230SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB imagesYahui Guo0Wei Zhou1Yongshuo H Fu2Fanghua Hao3Xuan Zhang4Le Xu5Ji Liu6Yuhong He7Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province, Central China Normal University, 430079, China; College of Urban and Environmental Sciences, Central China Normal University, China; The National Key Laboratory of Smart Farm Technology and Systems, ChinaKey Laboratory for Geographical Process Analysis&Simulation of Hubei Province, Central China Normal University, 430079, China; College of Urban and Environmental Sciences, Central China Normal University, ChinaKey Laboratory for Geographical Process Analysis&Simulation of Hubei Province, Central China Normal University, 430079, China; College of Urban and Environmental Sciences, Central China Normal University, China; College of Water Sciences, Beijing Normal University, Beijing 100875, China; Corresponding author.Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province, Central China Normal University, 430079, China; College of Urban and Environmental Sciences, Central China Normal University, China; College of Water Sciences, Beijing Normal University, Beijing 100875, ChinaCollege of Water Sciences, Beijing Normal University, Beijing 100875, ChinaCollege of Agriculture, Northeast Agricultural University, Harbin 150030, China; The National Key Laboratory of Smart Farm Technology and Systems, ChinaState Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710061, ChinaDepartment of Geography, Geomatics and Environment, University of Toronto, 3359 Mississauga Road, Mississauga, ON L 5L 1C6, CanadaWinter wheat lodging reduces wheat yield and poses a further risk to regional food security, emphasizing the need for timely and accurately monitoring of affected areas. Advances in Unmanned Aerial System (UAS) remote sensing and deep learning techniques provide new tools for detecting winter wheat lodging. In this study, the RGB images of winter wheat were captured by the DJI Phantom 4 Pro V2.0 at flight heights of 60 m (0.8cm/pixel), 90 m (1.8cm/pixel), 120 m (3.1cm/pixel), and 150 m (4.3cm/pixel). A novel SegNeXt-RCMSCA network was proposed by integrating horizontal and vertical pooling with a multi-scale self-calibrated convolution function to enhance global contextual information. The SegNeXt-RCMSCA model achieved an Intersection over Union (IoU) of 86.72 %, F1 score of 92.89 %, Recall of 94.33 %, and Precision of 91.49 %. The model was tested using images with different spatial resolutions, and the results indicated that the 60 m (0.8cm/pixel) achieved the highest detection accuracy. The proposed SegNeXt-RCMSCA demonstrated strong potential for detecting lodging in other crops, offering a robust tool for improving the crop management in precision agriculture. By enabling timely and accurately lodging detection, the model facilitates crop damage assessment, harvest optimization, and informed field management, while supporting large-scale agricultural monitoring and intelligent decision-making in precision farming.http://www.sciencedirect.com/science/article/pii/S2772375525004617UAS remote sensingWinter wheatLodging detectionDeep learning SegNeXt -RCMSCA networkPrecision agricultureImpact of spatial scale |
| spellingShingle | Yahui Guo Wei Zhou Yongshuo H Fu Fanghua Hao Xuan Zhang Le Xu Ji Liu Yuhong He SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images Smart Agricultural Technology UAS remote sensing Winter wheat Lodging detection Deep learning SegNeXt -RCMSCA network Precision agriculture Impact of spatial scale |
| title | SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images |
| title_full | SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images |
| title_fullStr | SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images |
| title_full_unstemmed | SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images |
| title_short | SegNeXt-RCMSCA: An improved SegNeXt network for detecting winter wheat lodging from UAS RGB images |
| title_sort | segnext rcmsca an improved segnext network for detecting winter wheat lodging from uas rgb images |
| topic | UAS remote sensing Winter wheat Lodging detection Deep learning SegNeXt -RCMSCA network Precision agriculture Impact of spatial scale |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525004617 |
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