Research On Hyperspectral Diagnosis Of Nitrogen And Potassium Elements Of The Grape Leaves

In this study,the reasons for the differences in spectra of different species and the effects of leaf flesh structure on spectra were analyzed by observing the leaf tissue structure through scanning electron microscopy using field experimental data and potted experimental data.The effects of different pre-processing on spectral data and leaf nitrogen and potassium content were analyzed by different data pre-processing of raw spectra.The most suitable model for estimation of nitrogen and potassium elements in grape leaves was found by selecting different parameters and testing the model using some field data and potted data.The conclusions were as follows:（1）Leaf SPAD values showed a trend of increasing and then decreasing with the increase of nitrogen application.The highest SPAD values were found for the optimal fertilization,and the regression equation of leaf nitrogen content index constructed from SPAD values reached a significant level,which indicating that SPAD can indicate the nitrogen content of grape leaves to some extent.（2）The spectral profiles of summer black,Italian,ruby,and autumn black at similar leaf nitrogen contents differed,with the leaf adaxial spectra showing Italian reflectance>ruby>summer black>autumn black in the near-infrared band.The reverse spectral curves of summer black and autumn black leaves were similar.The tissue structure of young leaves and old leaves of different varieties had large differences,mainly in the thickness of fenestrated and spongy tissues.（3）Some spectral parameters can improve the correlation on leaf nitrogen content after combining with leaf flesh tissue structure.The regression equations established by the indices constructed for different varieties after combining with leaf thickness have good significant levels.It can be concluded that leaf thickness can be considered as a parameter combined with spectral parameters to improve the diagnostic accuracy when conducting leaf spectral diagnosis.（4）The leaf adaxial spectrum was pretreated with dR and MSC+sdR to improve the correlation with the leaf nitrogen content,and the sensitive band range became larger;the leaf reverse spectrum was treated with dR and sdR to enhance the correlation with the leaf nitrogen content.After the correlation analysis between the original spectra of the front and back side and the nitrogen content of the leaf,it was found that the 400-1300 nm band was the nitrogen sensitive band.Among the spectral feature parameters constructed on the front side of the leaf,all of them reached a highly significant correlation with the leaf nitrogen content except SDy,λ_yandλ_o,among which Dr(r=-0.462^**)andλ_g(r=-0.495^**)had the best correlation.Among the parameters constructed on the reverse side of the leaf,the correlations reached significant levels except for RB,NRB,andλ_y,with Ro(r=-0.510^**),Dr(-0.524^**),andRg(-0.533^**)having the best correlation.Among them,PRI,PRI-2 and Dr parameters were generalized in the front and back spectra of grape leaves.（5）Model construction and validation of nitrogen content in grape leaves.The grape frontal spectra pretreated as dR+SNV had the most inscribed normalized index after the combination at 553 nm and 681 nm bands,with a linear fit equation of y=3.141-10.101x（R²=0.548,P<0.01）,and the regression equation reached a highly significant level.The model validation by regression analysis of well correlated parameters with the validation set showed that the model constructed by R_（732）,dR_（2161）for the adaxial spectrum andR_（717）,dR_（2164）,Dr,dR_（639）for the reverse spectrum for the estimation of nitrogen content in grape leaves was better,with the highest accuracy of the prediction model for the reverse Dr of leaves.The PLSR models were constructed with the spectral data as the independent variable and the leaf nitrogen content as the dependent variable.The PLSR models constructed by the pre-treatment of the obverse spectra were able to predict the nitrogen content of grape leaves more accurately except for the PLSR model constructed by the pre-treatment of MSC+sdR,which could only roughly estimate the nitrogen content of grape leaves,and the PLSR models constructed by the other pre-treatments can construct a more accurate prediction model,where the original spectrum at a factor of 13 is the best constructed PLSR model with a fittedR²of 0.936,RMSE of 1.7358,andRPD of 3.993 for the validation set.（6）Leaf spectra were significantly more correlated with leaf potassium content after first-order transformation treatment,and the sensitive band for leaf potassium content was 1300-2400 nm.Grape leaf adaxial spectra were more sensitive to leaf potassium content than the reverse spectra.The RPD values of the PLSR models for the spectral reflectance of grape leaves after the dR,dR+SNV,MSC+dR,sdR,sdR+SNV,and MSC+sdR treatments ranged from 1.567-1.849 among the leaf adaxial pretreatment methods,which indicating that the potassium content of leaves of different grape varieties could be estimated approximately,with the spectral reflectance constructed based on the second-order derivative treatment.The PLSR model constructed based on the spectral reflectance of the second-order derivative treatment was the best,with RPD=1.849 andR²=0.702.The PLSR estimation model for leaf potassium content constructed from the spectral reflectance of the R,dR,and dR+SNV treatments in the leaf reverse spectrum was better,with RPD greater than 1.4,and was able to make rough calculations of the potassium content of different grape variety leaves.