Prediction model for the water content of Lyceum barbarum tree canopy based on hyperspectral transformation
In order to achieve rapid and nondestructive monitoring of leaf water content in the canopy of Lyceum barbarum tree,Ningqi No.7 was taken as the research object to measure the spectra and water content of Lyceum barbarum canopy leaves.Two mathematical transformations(first-derivative and continuum removal)were carried out on the original spectra.Based on the correlation analysis between the original spectrum(OS),first-derivative spectra(FDS),continuum removal spectrum(CRS)and water content,sensitive wavelengths were selected.Random forest regression models(RFRM),partial least squares regression models(PLSRM),ridge regression models(RRM)and univariate regression models(URM)were constructed.Subsequently,the accuracy of these models was tested and evaluated.The results indicate that the fitting degrees of the FDS-based models and CRS-based models range from 0.716 to 0.938 and from 0.710 to 0.920,respectively,while OS-based models range from 0.710 to 0.874.It is evident that FDS-based and CRS-based models have a higher fitting degree than OS-based ones.From the analysis of model types,the random forest regression models(RFRM)exhibit the best fitting degree at 0.874-0.938,followed by partial least squares regression models(PLSRM)at 0.826-0.866 and ridge regression models(RRM)at 0.737-0.889,then the univariate regression models(URM)at 0.710-0.730 with the worst fit.A comprehensive analysis reveals that the random forest regression model based on first-derivative spectra(FDR+RFRM)has the best prediction effect.The fitting degree of the training datasets and the test datasets are 0.938 and 0.893,respectively,and the R2,RMSE,MAE and RPD of validation datasets are 0.872,0.561,0.466 and 2.156,respectively.It is concluded that a hyperspectral detection model with high prediction accuracy is developed by combining spectral transformation with machine learning,which is suitable for monitoring the water content of Lyceum barbarum canopy leaves.This provides a suitable and efficient method for monitoring the water content of Lyceum barbarum canopy.
water contentLyceum barbarumhyperspectralpartial least squares regression modelrandom forest regression modelridge regression model
万方数据
维普
