{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:17:00Z","timestamp":1784215020387,"version":"3.55.0"},"reference-count":76,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,5]],"date-time":"2020-09-05T00:00:00Z","timestamp":1599264000000},"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":"the 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>The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents\u2019 estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g\/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g\/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.<\/jats:p>","DOI":"10.3390\/s20185055","type":"journal-article","created":{"date-parts":[[2020,9,6]],"date-time":"2020-09-06T23:12:49Z","timestamp":1599433969000},"page":"5055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV"],"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\/0000-0003-4130-6981","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":"Zhaofei","family":"Wu","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":"Shuxin","family":"Wang","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&amp; 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\/0000-0002-1737-7985","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"}]},{"given":"Christopher","family":"Robin Bryant","sequence":"additional","affiliation":[{"name":"The School of Environmental Design and Rural Development, University of Guelph, Guelph, ON N1G 2W1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[[2020,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"014002","DOI":"10.1088\/1748-9326\/2\/1\/014002","article-title":"Global scale climate-crop yield relationships and the impacts of recent warming","volume":"2","author":"Lobell","year":"2007","journal-title":"Environ. 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