{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T19:52:18Z","timestamp":1773431538293,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T00:00:00Z","timestamp":1632787200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UK Science and Technology Facilities Council (STFC) under Newton fund","award":["ST\/V00137X\/1"],"award-info":[{"award-number":["ST\/V00137X\/1"]}]},{"name":"Fundamental Research Funds for the China Central Universities of USTB","award":["FRF-DF-19-002"],"award-info":[{"award-number":["FRF-DF-19-002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop disease is widely considered as one of the most pressing challenges for food crops, and therefore an accurate crop disease detection algorithm is highly desirable for its sustainable management. The recent use of remote sensing and deep learning is drawing increasing research interests in wheat yellow rust disease detection. However, current solutions on yellow rust detection are generally addressed by RGB images and the basic semantic segmentation algorithms (e.g., UNet), which do not consider the irregular and blurred boundary problems of yellow rust area therein, restricting the disease segmentation performance. Therefore, this work aims to develop an automatic yellow rust disease detection algorithm to cope with these boundary problems. An improved algorithm entitled Ir-UNet by embedding irregular encoder module (IEM), irregular decoder module (IDM) and content-aware channel re-weight module (CCRM) is proposed and compared against the basic UNet while with various input features. The recently collected dataset by DJI M100 UAV equipped with RedEdge multispectral camera is used to evaluate the algorithm performance. Comparative results show that the Ir-UNet with five raw bands outperforms the basic UNet, achieving the highest overall accuracy (OA) score (97.13%) among various inputs. Moreover, the use of three selected bands, Red-NIR-RE, in the proposed Ir-UNet can obtain a comparable result (OA: 96.83%) while with fewer spectral bands and less computation load. It is anticipated that this study by seamlessly integrating the Ir-UNet network and UAV multispectral images can pave the way for automated yellow rust detection at farmland scales.<\/jats:p>","DOI":"10.3390\/rs13193892","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T21:39:29Z","timestamp":1632865169000},"page":"3892","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0996-2586","authenticated-orcid":false,"given":"Tianxiang","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Shunde Graduate School, University of Science and Technology Beijing, Foshan 528000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3210-8839","authenticated-orcid":false,"given":"Zhiyong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3121-7208","authenticated-orcid":false,"given":"Jinya","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5895-6708","authenticated-orcid":false,"given":"Zhifang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2829-9369","authenticated-orcid":false,"given":"Cunjia","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK"}]},{"given":"Wen-Hua","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough LE11 3TU, UK"}]},{"given":"Jiangyun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China"},{"name":"Shunde Graduate School, University of Science and Technology Beijing, Foshan 528000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1007\/s12571-012-0200-5","article-title":"Crop Losses Due to Diseases and Their Implications for Global Food Production Losses and Food Security","volume":"4","author":"Savary","year":"2012","journal-title":"Food Secur."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1109\/TII.2020.2979237","article-title":"Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring","volume":"17","author":"Su","year":"2020","journal-title":"IEEE Trans. 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