{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,14]],"date-time":"2026-06-14T05:10:04Z","timestamp":1781413804293,"version":"3.54.1"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Science and Technology Planning Project of Guangdong Province","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Key Science and Technology Planning Project of Guangdong Province","award":["2020YFD1000905"],"award-info":[{"award-number":["2020YFD1000905"]}]},{"name":"Key Science and Technology Planning Project of Guangdong Province","award":["2022KJ136-05"],"award-info":[{"award-number":["2022KJ136-05"]}]},{"name":"Key Science and Technology Planning Project of Guangdong Province","award":["42005142"],"award-info":[{"award-number":["42005142"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2020YFD1000905"],"award-info":[{"award-number":["2020YFD1000905"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022KJ136-05"],"award-info":[{"award-number":["2022KJ136-05"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42005142"],"award-info":[{"award-number":["42005142"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Technical System of Peanut and Soybean Industry","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Guangdong Technical System of Peanut and Soybean Industry","award":["2020YFD1000905"],"award-info":[{"award-number":["2020YFD1000905"]}]},{"name":"Guangdong Technical System of Peanut and Soybean Industry","award":["2022KJ136-05"],"award-info":[{"award-number":["2022KJ136-05"]}]},{"name":"Guangdong Technical System of Peanut and Soybean Industry","award":["42005142"],"award-info":[{"award-number":["42005142"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020YFD1000905"],"award-info":[{"award-number":["2020YFD1000905"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022KJ136-05"],"award-info":[{"award-number":["2022KJ136-05"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42005142"],"award-info":[{"award-number":["42005142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vegetation. However, the information of UAV image data is relatively complex and difficult to analyze, which is the main problem limiting its large-scale use at present. In order to monitor plant physiological indexes from the multi-spectral data, a new method based on machine learning is studied in this paper. Using UAV for deriving the absorption coefficients of plant canopies and whole leaf area, this paper quantifies the effects of plant physiological indicators such as the soil and plant analyzer development (SPAD) value, whole leaf area, and dry matter accumulation on the relationship between the reflectance spectra. Nine vegetation indexes were then extracted as the sensitive vegetation indexes of the rice physiological indicators. Using the SVM model to predict the SPAD value of the plant, the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) values of the model were 1.90, 1.38, 0.13, 0.86, and 4.13, respectively. The results demonstrate that the rice plants display a considerable biochemical and spectral correlation. Using SVM to predict the SPAD value has a better effect because of a better adaptation and a higher accuracy than other models. This study suggests that the multi-spectral data acquired using UAV can quickly estimate field physiological indicators, which has potential in the pre-visual detection of SPAD value information in the field. At the same time, it can also be extended to the detection and inversion of other key variables of crops.<\/jats:p>","DOI":"10.3390\/rs15020453","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T03:11:02Z","timestamp":1673493062000},"page":"453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Quantification of Physiological Parameters of Rice Varieties Based on Multi-Spectral Remote Sensing and Machine Learning Models"],"prefix":"10.3390","volume":"15","author":[{"given":"Shiyuan","family":"Liu","sequence":"first","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of New Technology for Rice Breeding, Rice Research Institute of Guangdong Academy of Agricultural Science, Guangzhou 510640, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiguang","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongda","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangtao","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzhe","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4611-3417","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Agriculture, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s40003-011-0003-5","article-title":"Rice blast management through host-plant resistance: Retrospect and prospects","volume":"1","author":"Sarma","year":"2012","journal-title":"Agric. 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