{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T14:55:54Z","timestamp":1775832954707,"version":"3.50.1"},"reference-count":109,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T00:00:00Z","timestamp":1646784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Funds for Distinguished Young Youths","award":["No. 42025101"],"award-info":[{"award-number":["No. 42025101"]}]},{"name":"the National Key Research and Development Program of China","award":["No.2017YFA06036001"],"award-info":[{"award-number":["No.2017YFA06036001"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 31770516"],"award-info":[{"award-number":["No. 31770516"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the 111 Project","award":["No. B18006"],"award-info":[{"award-number":["No. B18006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precisely monitoring the growth condition and nutritional status of maize is crucial for optimizing agronomic management and improving agricultural production. Multi-spectral sensors are widely applied in ecological and agricultural domains. However, the images collected under varying weather conditions on multiple days show a lack of data consistency. In this study, the Mini MCA 6 Camera from UAV platform was used to collect images covering different growth stages of maize. The empirical line calibration method was applied to establish generic equations for radiometric calibration. The coefficient of determination (R2) of the reflectance from calibrated images and ASD Handheld-2 ranged from 0.964 to 0.988 (calibration), and from 0.874 to 0.927 (validation), respectively. Similarly, the root mean square errors (RMSE) were 0.110, 0.089, and 0.102% for validation using data of 5 August, 21 September, and both days in 2019, respectively. The soil and plant analyzer development (SPAD) values were measured and applied to build the linear regression relationships with spectral and textural indices of different growth stages. The Stepwise regression model (SRM) was applied to identify the optimal combination of spectral and textural indices for estimating SPAD values. The support vector machine (SVM) and random forest (RF) models were independently applied for estimating SPAD values based on the optimal combinations. SVM performed better than RF in estimating SPAD values with R2 (0.81) and RMSE (0.14), respectively. This study contributed to the retrieval of SPAD values based on both spectral and textural indices extracted from multi-spectral images using machine learning methods.<\/jats:p>","DOI":"10.3390\/rs14061337","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T02:10:35Z","timestamp":1646878235000},"page":"1337","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":128,"title":["Machine Learning-Based Approaches for Predicting SPAD Values of Maize Using Multi-Spectral Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0099-0759","authenticated-orcid":false,"given":"Yahui","family":"Guo","sequence":"first","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3156-7953","authenticated-orcid":false,"given":"Shouzhi","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"given":"Xinxi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8299-324X","authenticated-orcid":false,"given":"Mario","family":"Cunha","sequence":"additional","affiliation":[{"name":"Sciences Faculty, Porto University, Rua do Campo Alegre, 4169-007 Porto, Portugal"},{"name":"Institute for Systems and Computer Engineering, Technology and Science (INESC TEC), Campus da Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1737-7985","authenticated-orcid":false,"given":"Senthilnath","family":"Jayavelu","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore"}]},{"given":"Davide","family":"Cammarano","sequence":"additional","affiliation":[{"name":"Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-5292","authenticated-orcid":false,"given":"Yongshuo","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Guo, Y., Yin, G., Sun, H., Wang, H., Chen, S., Senthilnath, J., Wang, J., and Fu, Y. 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