## 3 Results and Discussion
### Fusion Model Building and Discriminant  
#### 3.3.1 Discriminant Model Based on Spectral Data Layer Fusion  
The remaining 156 samples were randomly divided into a calibration set and prediction set in the ratio of 2:1. There were 104 samples in calibration sets including 20 with slight citrus greening, 20 with moderate citrus greening, 20 with serious citrus greening, 22 nutrient deficiency and 22 normal leaves. The remaining 52 were set as prediction samples used to evaluate the prediction effect of the model [52; 53]. Classification vector values were assigned according to the disease characteristics of the samples, which were set as [2; 4; 6; 8; 10] in the experiment, 2 as light HLB leaf, 4 as medium HLB leaf, 6 as heavy HLB leaf, 8 as deficiency leaf and 10 as normal leaf, and the intermediate value of the two vectors was the classification threshold [30]. Figure 4: Representative spectra of 5 kinds of leaves after normalization. The model was developed from the spectral data fusion between the range of 500-1000 nm of visible and the range of 1000-2500 nm of NIR. The data fusion was carried out by using three strategies: spectrum direct stitching, derivative stitching and normalization stitching. By comparative analysis among those three leaves, no bright band was shown. Different from those of leaves with citrus greening, the bright bands of nutrient deficiency leaves may have a connection to the lack of nutrients in leaves [28; 29]. More details were listed in Table 1.  
There were 2220 variables in the data fusion model, including 665 visible band variables and 1555 near-infrared band variables. As shown in Table 2, the identification accuracy of the LS-SVM model, based on the direct splicing of spectral data, is better than the prediction results of single bands of 500-1000 and 1000-2500 nm. Although the parameters \(\gamma\), \(\sigma^{2}\) of the LS-SVM fusion model is larger and their operation time is longer, the identification accuracy of the model is higher than those of the two single bands. The model of spectral direct splicing after the second derivative pretreatment is better with 100% identification accuracy and reduced penalty coefficient \(\gamma\) and kernel parameter \(\sigma^{2}\), where the penalty coefficient \(\gamma\) of the linear kernel function is reduced to 0.09, and the operation time is also shortened. The identification accuracy of the model using linear function Lin_Kernel as the kernel function is better than that of using radial basis function RBF_Kernel, which may be due to a linear relationship between spectral variables. Therefore, it is feasible to develop the identification model of citrus greening through derivative direct splicing strategy on the basis of spectral data fusion. The better effect of the model is mainly because the data of the two bands in the range of 500-1000 and 1000-2500 nm contain more effective information. Secondly, because of the dimensional difference of spectral data at two bands of 500-1000 and 1000-2500 nm, it is necessary to use normalization to remove spectral data dimension on the basis of second derivative, and then splice the spectrum. It can be concluded from Table 2 that the result of normalization splicing is better than direct splicing, but not as good as second derivative direct splicing. The main reason is that normalization may lead to the loss of spatial information of the identification pattern, so the result of second derivative splicing is optimal. Based on the best model in Table 2, the aforementioned samples were randomly divided into calibration set and prediction set according to different proportions. Combined with partial least squares discriminant analysis, a diagnosis model of citrus greening disease with the fusion of four spectral data layers was constructed, and the results were shown in Table 3.