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

文献类型： 外文期刊

作者： An, Gangqiang 1 ; Xing, Minfeng 1 ; He, Binbin 1 ; Liao, Chunhua 3 ; Huang, Xiaodong 4 ; Shang, Jiali 5 ; Kang, Haiqi 6 ;

作者机构： 1.Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China

2.Univ Elect Sci & Technol China, Ctr Informat & Geosci, Chengdu 611731, Peoples R China

3.Western Univ, Dept Geog, London, ON N6A 5C2, Canada

4.Appl Geosolut, 15 Newmarket Rd, Durham, NH 03824 USA

5.Agr & Agri Food Canada, Ottawa Res & Dev Ctr, 960 Carling Ave, Ottawa, ON K1A 0C6, Canada

6.Sichuan Acad Agr Sci, Crop Res Inst, Chengdu 610066, Peoples R China

关键词： hyperspectral remote sensing; machine learning technology; RCRWa-b; SPAD value; rice

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN：

年卷期： 2020 年 12 卷 18 期

页码：

收录情况： SCI

摘要： 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 (R-2). 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 R-2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R-2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R-2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R-2 = 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.

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