{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T12:06:05Z","timestamp":1783512365348,"version":"3.55.0"},"reference-count":94,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T00:00:00Z","timestamp":1599609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the General Program of National Nature Science Foundation of China","award":["Grant No. 31770516"],"award-info":[{"award-number":["Grant No. 31770516"]}]},{"name":"the National Key Research and Development Program of China","award":["2017YFA06036001"],"award-info":[{"award-number":["2017YFA06036001"]}]},{"name":"111 Project (B18006) and Fundamental Research Funds for the Central Universities","award":["2018EYT05"],"award-info":[{"award-number":["2018EYT05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red\u2013green\u2013blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 \u00d7 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture.<\/jats:p>","DOI":"10.3390\/s20185130","type":"journal-article","created":{"date-parts":[[2020,9,9]],"date-time":"2020-09-09T09:01:09Z","timestamp":1599642069000},"page":"5130","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0099-0759","authenticated-orcid":false,"given":"Yahui","family":"Guo","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3926-2609","authenticated-orcid":false,"given":"Guodong","family":"Yin","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyong","family":"Sun","sequence":"additional","affiliation":[{"name":"The Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, The Chinese Academy of Sciences, 286 Huaizhong Road, Shijiazhuang 050021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4130-6981","authenticated-orcid":false,"given":"Hanxi","family":"Wang","sequence":"additional","affiliation":[{"name":"State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration\/School of Environment, Northeast Normal University, Jingyue Street 2555, Changchun 130017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouzhi","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1737-7985","authenticated-orcid":false,"given":"J.","family":"Senthilnath","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8332-7997","authenticated-orcid":false,"given":"Jingzhe","family":"Wang","sequence":"additional","affiliation":[{"name":"MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area of the Ministry of Natural Resources &amp; Guangdong Key Laboratory of Urban Informatics &amp; Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-5292","authenticated-orcid":false,"given":"Yongshuo","family":"Fu","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,9]]},"reference":[{"key":"ref_1","unstructured":"FAO, IFAD, UNICEF, WFP, and WHO (2019). 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