Assessing different regression algorithms for paddy rice leaf nitrogen concentration estimations from the first-derivative fluorescence spectrum

Abstract

The non-destructive and rapid estimation of the crop's leaf nitrogen concentration (LNC) is significant for the quality evaluation and precise management of nitrogen (N) fertilizer. First derivative can be applied to reduce the noise in the spectral analysis, which is suited to estimate leaf N and chlorophyll concentration with different fertilization levels. In this study, the first-derivative fluorescence spectrum (FDFS) was calculated in terms of the laser-induced fluorescence (LIF) spectra and was combined with different regression algorithms, including principal component analysis (PCA), partial least-square regression (PLSR), random forest (RF), radial basic function neural network (RBF-NN), and back-propagation neural network (BPNN) for paddy rice LNC estimation. Then, the effect of diverse inner parameters on regression algorithm for LNC estimation based on the calculated FDFS served as input variables were discussed, and the optimal parameters of each model were acquired. Subsequently, the performance of different models (PLSR, RF, BPNN, RBF-NN, PCA-RF, PCA-BPNN, and PCA-RBFNN) with the optimal parameter for LNC estimation based on FDFS was discussed. Results demonstrated that PCA can efficiently extract major spectral information without obviously losing, which can improve the stability and robustness of model (PLSR, PCA-RF, PCA-BNN, and PCA-RBFNN) for LNC estimation. Then, PCA-RBFNN model exhibited better potential for LNC estimation with higher average R² (R²=0.8743) and lower SD values (SD=0.0256) than that the other regression models in this study. And, PLSR also exhibited promising potential for LNC estimation in which the R² values (average R²=0.8412) are higher than that the other models except for PCA-RBFNN.