{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T14:50:24Z","timestamp":1784213424395,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871344"],"award-info":[{"award-number":["41871344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["XDA28040502"],"award-info":[{"award-number":["XDA28040502"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["41871344"],"award-info":[{"award-number":["41871344"]}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA28040502"],"award-info":[{"award-number":["XDA28040502"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Predicting leaf nitrogen content (LNC) using unmanned aerial vehicle (UAV) images is of great significance. Traditional LNC prediction methods based on empirical and mechanistic models have limitations. This study aimed to propose a new LNC prediction method based on combining deep learning methods and mechanistic models. Wheat field experiments were conducted to make plants with different LNC values. The LNC and UAV hyperspectral images were collected during the critical growth stages of wheat. Based on these data, a method combining the deep multitask learning method and the N-based PROSAIL model was proposed and compared with traditional LNC prediction methods, including spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods. The results show that the new proposed method obtained the best LNC prediction results, with R2, RMSE and RMSE% values of 0.79, 20.86 \u03bcg\/cm2 and 18.63%, respectively, during calibration and 0.82, 18.40 \u03bcg\/cm2 and 16.92%, respectively, during validation. The other methods obtained R2, RMSE and RMSE% values between 0.29 and 0.68, 25.71 and 38.52 \u03bcg\/cm2 and 22.95 and 34.39%, respectively, during calibration and between 0.43 and 0.74, 22.79 and 33.55 \u03bcg\/cm2 and 20.96 and 30.86%, respectively, during validation. Thus, this study provides an accurate LNC prediction tool for precise nitrogen (N) management in the field.<\/jats:p>","DOI":"10.3390\/rs14246334","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T03:01:51Z","timestamp":1671073311000},"page":"6334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiao","family":"Ma","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, 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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4560-0045","authenticated-orcid":false,"given":"Pengfei","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"National Earth System Science Data Center, National Science and Technology Infrastructure of China, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-214X","authenticated-orcid":false,"given":"Xiuliang","family":"Jin","sequence":"additional","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.scienta.2016.04.026","article-title":"Crop responses to nitrogen overfertilization: A review","volume":"205","author":"Albornoz","year":"2016","journal-title":"Sci. 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