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Article

Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data

1
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Center for Information and Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
3
Department of Geography, Western University, London, ON N6A 5C2, Canada
4
Applied Geosolutions, 15 Newmarket Road, Durham, NH 03824, USA
5
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada
6
Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(18), 3104; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183104
Received: 6 August 2020 / Revised: 19 September 2020 / Accepted: 20 September 2020 / Published: 22 September 2020
(This article belongs to the Special Issue Remote Sensing for Precision Agriculture)
Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice. View Full-Text
Keywords: hyperspectral remote sensing; machine learning technology; RCRWa-b; SPAD value; rice hyperspectral remote sensing; machine learning technology; RCRWa-b; SPAD value; rice
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MDPI and ACS Style

An, G.; Xing, M.; He, B.; Liao, C.; Huang, X.; Shang, J.; Kang, H. Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data. Remote Sens. 2020, 12, 3104. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183104

AMA Style

An G, Xing M, He B, Liao C, Huang X, Shang J, Kang H. Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data. Remote Sensing. 2020; 12(18):3104. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183104

Chicago/Turabian Style

An, Gangqiang, Minfeng Xing, Binbin He, Chunhua Liao, Xiaodong Huang, Jiali Shang, and Haiqi Kang. 2020. "Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data" Remote Sensing 12, no. 18: 3104. https://0-doi-org.brum.beds.ac.uk/10.3390/rs12183104

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