{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:17:12Z","timestamp":1781101032419,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T00:00:00Z","timestamp":1630800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Major Project of China\u02b9s High 417 Resolution Earth Observation System","award":["21\u2010Y20B01\u20109001\u201019\/22"],"award-info":[{"award-number":["21\u2010Y20B01\u20109001\u201019\/22"]}]},{"name":"The National Natural 418 Science Foundation of China","award":["41871344"],"award-info":[{"award-number":["41871344"]}]},{"name":"The Strategic Priority Research Pro419 gram of the Chinese Academy of Sciences","award":["XDA23100101"],"award-info":[{"award-number":["XDA23100101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.<\/jats:p>","DOI":"10.3390\/rs13173526","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T13:18:26Z","timestamp":1630934306000},"page":"3526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4560-0045","authenticated-orcid":false,"given":"Pengfei","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"},{"name":"National Earth System Science Data Center, National Science and Technology Infrastructure of China, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangyong","family":"Wang","sequence":"additional","affiliation":[{"name":"Cotton Institute, Xinjiang Academy of Agricultural and Reclamation Science, Shihezi 832000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"779","DOI":"10.5424\/sjar\/2009074-1092","article-title":"Review. 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