{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:50:06Z","timestamp":1773939006757,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T00:00:00Z","timestamp":1627257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["41901353"],"award-info":[{"award-number":["41901353"]}]},{"name":"National Nature Science Foundation of China","award":["31601251"],"award-info":[{"award-number":["31601251"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 2662019QD054; No.2662019PY057; 2662017QD038"],"award-info":[{"award-number":["No. 2662019QD054; No.2662019PY057; 2662017QD038"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC1502406-03"],"award-info":[{"award-number":["2017YFC1502406-03"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Fund of Guangxi Key Laboratory of Water Engineering Materials and Structures, Guangxi Institute of water resources research","award":["GXHRI-WEMS-2019-03"],"award-info":[{"award-number":["GXHRI-WEMS-2019-03"]}]},{"name":"National Key Research and Development Program of Guangxi","award":["2019AB20009"],"award-info":[{"award-number":["2019AB20009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicles-collected (UAVs) digital red\u2013green\u2013blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76\u20130.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92\u20130.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52\u20130.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00\u20130.40 and R2 = 0.55\u20130.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g\/m2 and R2 = 0.89, RMSE = 39.82 g\/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.<\/jats:p>","DOI":"10.3390\/rs13152937","type":"journal-article","created":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T22:22:46Z","timestamp":1627338166000},"page":"2937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red\u2013Green\u2013Blue Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6659-2702","authenticated-orcid":false,"given":"Linglin","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Guozhang","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4756-9934","authenticated-orcid":false,"given":"Ran","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Interdisciplinary Sciences Research Institute, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Jianguo","family":"Man","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Crop Genetic Improvement, MARA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Weibo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"},{"name":"Guanxi Key Laboratory of Water Engineering Materials and Structures, Guanxi Institute of Water Resources Research, Nanning 530023, China"}]},{"given":"Binyuan","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Zhengang","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]},{"given":"Rui","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.isprsjprs.2017.05.003","article-title":"Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery","volume":"130","author":"Zhou","year":"2017","journal-title":"ISPRS J. 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