Predicting Leaf Nitrogen Content in Wolfberry Trees Using Hyperspectral Reflectance
To realize rapid and non-destructive monitoring of nitrogen content in wolfberry tree,"Ningqi No.7"wolfberry was selected as the research object to synchronously measure the spectral reflectance and nitrogen content of wolfberry leaves.Fast fourier transform(FFT)was performed to smooth and filter the measured spectra to obtain the original spectra(OS).Three types of mathematical transformations including first-derivative(FD),second-derivative(SD)and continuum removal(CR)transformations were performed on the original spectra,and the corresponding spectral datasets including first-derivative spectra(FDS),second-derivative spectra(SDS)and continuum removal spectra(CRS)were obtained.The correlation analysis between the spectra including OS,FDS,SDS and CRS and nitrogen of wolfberry leaves were performed to select sensitive wavelengths based on the value of correlation coefficients.Random forest regression models(RFRM)and multiple linear regression models(MLRM)were constructed using selected sensitive wavelengths to predict the nitrogen content of wolfberry leaves.The study indicated that the prediction accuracy of RFRM and MLRM constructed using the three types of transformation spectra were better than those constructed using OS.Among them,the models constructed using FDS had the best prediction performance,followed by the models constructed using SDS and the models constructed using CRS,the models constructed using OS had the worst prediction accuracy.Meanwhile,it is shown that the prediction accuracy of RFRM were superior to MLRM.Compared to MLRM,the fitting degree of RFRM constructed using OS,FDS,SDS and CRS increased by 0.258,0.259,0.275 and 0.291,the root mean square error(RMSE)decreased by 0.044,0.054,0.059 and 0.076,and the mean absolute error(MAE)decreased by 0.045,0.043,0.066 and 0.059,respectively.The RFRM model constructed using the sensitive wavelengths selected from FDS had best accuracy and stability,with the determination coefficient,RMSE and MAE of calibration set of 0.897,0.071 and 0.058,respectively,with the determination coefficient,RMSE and MAE of validation set of 0.689,0.129 and 0.102,respectively.It can be used as a hyperspectral estimation method for leaf nitrogen content
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万方数据
