Abstract
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.
Highlights
In recent years, precision agriculture has been gaining momentum [1]
Y = f (x) = αiK(xi, x) i=1 where xi is the explanatory variable used at the training stage; n is the number of samples; αi is the weight coefficient, and K is the radial basis kernel function (RBF), whose equation is as follows: B
For the Gaussian process regression (GPR)-M, this model shows great generalization and robustness in estimating the SPAD value of rice by using four selected RCRWa-b when the data set is not large enough, which is mainly manifested in the R2, mean absolute error (MAE), and root mean square error (RMSE) of the predicted result for the training set being close to the R2, MAE, and RMSE of the predicted result for the validation set, respectively
Summary
Precision agriculture has been gaining momentum [1]. Rice is one of the three major food crops in the world and covers the largest planting area in China [2]. Many studies have developed a large number of spectral indices to estimate the chlorophyll content of crops based on hyperspectral remote sensing reflectance [9,10,11] Most of these spectral indices perform well in estimating crop chlorophyll content, it is difficult to interpret their physical meanings as most of them are in normalized radio or normalized difference form. This study has two major objects: (1) investigating the potential of the rate of change in reflectance between wavebands in estimating the chlorophyll content of rice; and (2) comparing the performance of four advanced machine learning technology, GP, RF, SVR and gradient boosting regression tree (GBRT), in estimating the chlorophyll content of rice
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