Hyperspectral quantitative inversion of chlorophyll content in pepper based on MCC-GAPLS-PLSR
In order to accurately monitor the growth of peppers,this study performed logarithmic treatment,re-ciprocal treatment,reciprocal logarithmic treatment,con-tinuum removal treatment,first derivative treatment,sec-ond derivate treatment on the canopy spectral reflectance of peppers,and conducted correlation analysis with SPAD values.The maximum correlation coefficient method(MCC)was used to select the feature bands with good correlation to generate a feature band dataset.And the genetic algorithm-partial least squares(GAPLS)was used to reduce the dimensionality to obtain the optimal feature band combination.Pepper chlo-rophyll content inversion model was constructed by using four machine learning algorithms:partial least squares regression(PLSR),backpropagation neural network(BPNN),random forest(RF)and least squares support vector machine(LSS-VM).The results showed that the optimal wavelengths and corresponding treatments were 700 nm(original reflectivity),699 nm(logarithmic treatment),713 nm(continuum removal treatment),500 nm(second derivate treatment),713 nm(second derivate treatment).The dimensionality reduction effect by GAPLS was good.And compared with before dimension reduction,the accuracy improvement rate of PLSR model was the highest,R2 and RPD increased by 82.22%and 136.98%respectively,and RMSE decreased by 29.96%.Among the four models,PLSR model after dimensionality reduction by GA-PLS had the best accuracy,with R2,RMSE,and RPD of 0.82,1.94,and 4.55,respectively.The MCC-GAPLS-PLSR model constructed in this study has good inversion potential and is suitable for measuring the chlorophyll content of pepper leaves in the study area,thus promoting efficient cultivation of peppers.
chlorophyll contentpepperhyperspectralspectral transformationgenetic algorithm-partial least squares
NETL
NSTL
万方数据
