## 3 Results and Discussion
### Fusion Model Building and Discriminant
#### 3.3.2 Discriminant Model Based on Data Feature Layer Fusion  
On the basis of PCA and SPA methods to screen characteristic variables, LS-SVM and PLS-DA recognition models of citrus greening disease were established, respectively, the diagnostic results were shown in Tables 4 and 5. When the Lin_Kernel function is used as the kernel function and the feature variables screened of second derivative spectral by PCA, the data fusion modeling results are the best, the recognition accuracy is 100%, the penalty coefficient is 0.09, and the operation time is 0.66 s. When SPA selects normalized second derivative spectral feature variables, the data fusion model has the worst recognition effect. The main cause for this phenomenon probably is that new feature variables acquired after PCA compressing variables contains more effective information, while some effective information may become lost during the SPA variable screening process; meanwhile, the normalized spectra may result in spatial information loss of identification pattern, reducing accuracy. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Selection Method**} & \multirow{2}{*}{**Wavelength Range**} & \multicolumn{3}{c}{**Kernel Function**} & \multirow{2}{*}{**Operation Time/s**} & \multirow{2}{*}{**Identification Accuracy/\%**} \\  & & & **Number** & & **Time/s** & \\ \hline \multirow{4}{*}{PCA} & 500–2500 & RBF\_Kernel & 11 & \(\gamma=251.44~{}\sigma^{2}=46.01\) & 1.63 & 100\% \\  & before normalized & Lin\_Kernel & 11 & \(\gamma=0.09\) & 0.66 & 100\% \\  & 500–2500 after & RBF\_Kernel & 11 & \(\gamma=2047.44~{}\sigma^{2}=101.57\) & 1.63 & 100\% \\  & normalized & Lin\_Kernel & 11 & \(\gamma=8.81\) & 2.05 & 100\% \\  & 500–2500 & RBF\_Kernel & 55 & \(\gamma=0.93~{}\sigma^{2}=6.11\) & 1.42 & 100\% \\  & before normalized & Lin\_Kernel & 55 & \(\gamma=0.19\) & 1.01 & 100\% \\  & 500–2500 after & RBF\_Kernel & 60 & \(\gamma=13.17~{}\sigma^{2}=70.27\) & 1.89 & 96\% \\  & normalized & Lin\_Kernel & 60 & \(\gamma=29.12\) & 1.39 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of spectral fusion LS-SVM model by different variable selection methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Samples in the Modeling Set**} & \multirow{2}{*}{**Prediction Set**} & \multirow{2}{*}{**Identification Accuracy (\%)**} & \multirow{2}{*}{**RMSEC (\%)**} & \multirow{2}{*}{**RMSEP (\%)**} & \multirow{2}{*}{**\(\mathbf{R_{\rm c}}\)**} & \multirow{2}{*}{**\(\mathbf{R_{\rm P}}\)**} & \multirow{2}{*}{**\(\mathbf{P_{\rm c}}\)**} \\  & & & & & & & & \\ \hline
126 & 30 & 100\% & 0.77 & 0.45 & 0.91 & 0.97 & 19 \\
117 & 39 & 100\% & 0.76 & 0.42 & 0.93 & 0.98 & 15 \\
104 & 52 & 100\% & 0.65 & 0.47 & 0.94 & 0.97 & 13 \\
94 & 62 & 100\% & 0.78 & 0.41 & 0.90 & 0.98 & 13 \\ \hline \multicolumn{7}{c}{NOTE: The “RMSEC” and “RMSEP” stand for corrected standard deviation and predicted standard deviation, respectively; “\(R_{\rm c}\)” and “\(R_{\rm P}\)” stand for prediction set correlation coefficient and ailibration set correlation coefficient, respectively; “\(\mathbf{P_{\rm c}}\)” represents the number of principal components.} \\ \end{tabular}
\end{table} 
Table 3: PLS-DA models with different proportions of modeling set and prediction set.