Inversion of pepper SPAD values from UAV hyperspectral data
In order to establish a more stable and predictive inversion model of chlorophyll content in pepper,this study analyzed the correlation between the original spectrum and other transformation spectra and the chlorophyll relative content(SPAD value)based on the UAV hyperspectral data and SPAD value.The maximum correlation coefficient method(MCC)was used to select the feature bands with good correlation to generate the feature band dataset,and then the genetic algorithm-partial least squares method(GAPLS)was used to reduce the dimension to obtain the optimal feature band combination.Five machine learning algorithms,namely partial least squares(PLSR),backpropagation neural network(BPNN),random forest(RF),least squares support vector machine(LSSVM)and genetic algorithm optimized least squares support vector machine(GA-LSSVM),were used to construct a chlorophyll content inversion model of pepper.The results showed that the SPAD value of pepper leaves was inversely proportional to the hyperspectral reflectance.The sensitive band of chlorophyll in pepper was mainly concentrated in 400～700 nm.The first-order differential spectra had the best correlation with the SPAD value,and the second-order differential spectra at 671 nm wavelength had the largest negative correlation with chlorophyll content,with a correlation coefficient of-0.69.The models based on reciprocal logarithmic spectra generally had high accuracy.The best performance of the model was the GA-LSSVM model based on differential spectroscopy,with the coefficient of determination(R2),root mean square error(RMSE)and relative analysis error(RPD)values of 0.84,1.41 and 2.24,respectively,followed by the RF model based on reciprocal logarithmic spectroscopy,with R2,RMSE and RPD values of 0.83,1.57 and 2.13,respectively.
万方数据
维普
