{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T03:04:37Z","timestamp":1780369477164,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T00:00:00Z","timestamp":1694908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chongqing Meteorological Department Operational Technical Research Project","award":["YWJSGG-202319"],"award-info":[{"award-number":["YWJSGG-202319"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wheat lodging has a significant impact on yields and quality, necessitating the accurate acquisition of lodging information for effective disaster assessment and damage evaluation. This study presents a novel approach for wheat lodging detection in large and heterogeneous fields using UAV remote sensing images. A comprehensive dataset spanning an area of 2.3117 km2 was meticulously collected and labeled, constituting a valuable resource for this study. Through a comprehensive comparison of algorithmic models, remote sensing data types, and model frameworks, this study demonstrates that the Deeplabv3+ model outperforms various other models, including U-net, Bisenetv2, FastSCN, RTFormer, Bisenetv2, and HRNet, achieving a noteworthy F1 score of 90.22% for detecting wheat lodging. Intriguingly, by leveraging RGB image data alone, the current model achieves high-accuracy rates in wheat lodging detection compared to models trained with multispectral datasets at the same resolution. Moreover, we introduce an innovative multi-branch binary classification framework that surpasses the traditional single-branch multi-classification framework. The proposed framework yielded an outstanding F1 score of 90.30% for detecting wheat lodging and an accuracy of 86.94% for area extraction of wheat lodging, surpassing the single-branch multi-classification framework by an improvement of 7.22%. Significantly, the present comprehensive experimental results showcase the capacity of UAVs and deep learning to detect wheat lodging in expansive areas, demonstrating high efficiency and cost-effectiveness under heterogeneous field conditions. This study offers valuable insights for leveraging UAV remote sensing technology to identify post-disaster damage areas and assess the extent of the damage.<\/jats:p>","DOI":"10.3390\/rs15184572","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T23:32:27Z","timestamp":1694993547000},"page":"4572","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Efficient Wheat Lodging Detection Using UAV Remote Sensing Images and an Innovative Multi-Branch Classification Framework"],"prefix":"10.3390","volume":"15","author":[{"given":"Kai","family":"Zhang","sequence":"first","affiliation":[{"name":"Jiangjin Meteorological Bureau, China Meteorological Administration Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing 402260, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rundong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqian","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2391-0782","authenticated-orcid":false,"given":"Jie","family":"Deng","sequence":"additional","affiliation":[{"name":"Jiangjin Meteorological Bureau, China Meteorological Administration Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing 402260, China"},{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3397-7195","authenticated-orcid":false,"given":"Ahsan","family":"Abdullah","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Congying","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Kaifeng Experimental Station, China Agricultural University, Kaifeng 475000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4910-5001","authenticated-orcid":false,"given":"Zhanhong","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Plant Pathology, College of Plant Protection, China Agricultural University, Beijing 100193, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"ref_1","unstructured":"(2023, June 25). 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