{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:00:32Z","timestamp":1779379232621,"version":"3.53.1"},"reference-count":53,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T00:00:00Z","timestamp":1597622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program","award":["2018YFD0300505-1"],"award-info":[{"award-number":["2018YFD0300505-1"]}]},{"name":"the National Natural Science Fund","award":["31971785"],"award-info":[{"award-number":["31971785"]}]},{"name":"the National Natural Science Fund","award":["31501219"],"award-info":[{"award-number":["31501219"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2020TC036"],"award-info":[{"award-number":["2020TC036"]}]},{"name":"the Graduate Training Project of China Agricultural University","award":["JD2019004"],"award-info":[{"award-number":["JD2019004"]}]},{"name":"the Graduate Training Project of China Agricultural University","award":["YW2020007"],"award-info":[{"award-number":["YW2020007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In order to improve the diagnosis accuracy of chlorophyll content in maize canopy, the remote sensing image of maize canopy with multiple growth stages was acquired by using an unmanned aerial vehicle (UAV) equipped with a spectral camera. The dynamic influencing factors of the canopy multispectral images of maize were removed by using different image segmentation methods. The chlorophyll content of maize in the field was diagnosed. The crop canopy spectral reflectance, coverage, and texture information are combined to discuss the different segmentation methods. A full-grown maize canopy chlorophyll content diagnostic model was created on the basis of the different segmentation methods. Results showed that different segmentation methods have variations in the extraction of maize canopy parameters. The wavelet segmentation method demonstrated better advantages than threshold and ExG index segmentation methods. This method segments the soil background, reduces the texture complexity of the image, and achieves satisfactory results. The maize canopy multispectral band reflectance and vegetation index were extracted on the basis of the different segmentation methods. A partial least square regression algorithm was used to construct a full-grown maize canopy chlorophyll content diagnostic model. The result showed that the model accuracy was low when the image background was not removed (Rc2 (the determination coefficient of calibration set) = 0.5431, RMSEF (the root mean squared error of forecast) = 4.2184, MAE (the mean absolute error) = 3.24; Rv2 (the determination coefficient of validation set) = 0.5894, RMSEP (the root mean squared error of prediction) = 4.6947, and MAE = 3.36). The diagnostic accuracy of the chlorophyll content could be improved by extracting the maize canopy through the segmentation method, which was based on the wavelet segmentation method. The maize canopy chlorophyll content diagnostic model had the highest accuracy (Rc2 = 0.6638, RMSEF = 3.6211, MAE = 2.89; Rv2 = 0.6923, RMSEP = 3.9067, and MAE = 3.19). The research can provide a feasible method for crop growth and nutrition monitoring on the basis of the UAV platform and has a guiding significance for crop cultivation management.<\/jats:p>","DOI":"10.3390\/rs12162650","type":"journal-article","created":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T21:58:53Z","timestamp":1597701533000},"page":"2650","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery"],"prefix":"10.3390","volume":"12","author":[{"given":"Lang","family":"Qiao","sequence":"first","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dehua","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan University of Animal Husbandry and Economy, Zhengzhou 450044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minzan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyong","family":"Ma","sequence":"additional","affiliation":[{"name":"Dry-Land Farming Institute of Hebei Academy of Agricultural and Forestry Sciences, Hengshui 053000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non\u2013destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. 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