## 3 Results and Analysis
### Accuracy Comparison and Analysis of Predictive Models  
After spectral raw data preprocessing and characteristic wavelength selection, the regression algorithm was programmed with MATLAB software, with four prediction models of PLS, Si-PLS, SVR, and Si-SVR established, respectively. The prediction performance of the model was analyzed by using \(RMSEC\), \(RMSEP\), \(Rc\), \(Rp\), and \(RPD\) values as response indicators. In addition, a comparison was drawn between the PLS, Si-PLS, SVR, and Si-SVR models. To ensure comparability, the same independent samples were used for verification. Figure 6 shows the results of estimating soil N content through the sample spectra. Figure 5: The schematic diagram of characteristic wavelength selection. According to the prediction results produced by the hyperspectral prediction model of soil N content on the basis of regression analysis, the SVR model performs best in terms of modeling effect, with the modeling determination coefficient \(Rc\) value being 0.9762 and \(RMSEC\) value being 0.0159%. The second-best performer is the Si-SVR model, with the modeling determination coefficient \(Rc\) value being 0.9529 and the \(RMSEC\) value being 0.0201%. In terms of model verification effect, the Si-SVR model performs best, with the \(RPD\) value being 2.86. The determination coefficient \(Rc\) value of PLS model verification is 0.8988, while the \(RMSEP\) and \(RPD\) values are 0.0331% and 2.04%, respectively. The Si-SVR model, SVR model, and Si-PLS model outperform the PLS model in terms of verification results. Although the PLS model is the simplest, its accuracy is relatively poor. Compared with the Si-PLS model and the SVR model, the relative standard deviation \(RPD\) value of the Si-SVR model is 2.86, which exceeds 2.0, and the level of its prediction accuracy is high. When the prediction accuracy of soil nitrogen content reaches a satisfactory level, it is preferred as the estimation method. Figure 7 shows the model used to estimate soil P content based on the modeled sample data. According to the results produced by the hyperspectral prediction model of soil phosphorus content based on regression analysis, the modeling effects of Si-SVR and SVR are highly comparable, with the termination coefficient \(Rc\) of Si-SVR modeling being 0.9106 and the \(RMSEC\) value being 1.0622%. The determination coefficient \(Rc\) value of SVR model modeling is 0.9065, and the \(RMSEC\) value is 1.0837%. In terms of model verification effect, SVR model performs best, with \(RPD\) being 2.23. The \(RPD\) value of Si-SVR model also exceeds 2.0, reaching 2.12. The two models perform well in quantitative prediction effect and achieve a high prediction accuracy for P. When this method can be used to meet the requirement on prediction accuracy for soil available phosphorus content, these two methods are preferable. The model used to estimate soil K content based on the modeled sample data is shown in Figure 8.  
Figure 6: Scatter plots of measured and predicted values of nitrogen (N) content. According to the results produced by the hyperspectral prediction model of soil K content based on regression analysis, the SVR model performs best in modeling effect, with the modeling determination coefficient \(Rc\) value being 0.9291 and \(RMSEC\) value being 5.2097%. The second-best performer is the Si-SVR model, with the modeling determination coefficient \(Rc\) value being 0.9289 and the \(RMSEC\) value being 5.2247%. In terms of model  
Figure 8: Scatter plot of measured and predicted values of potassium (K) content. Figure 7: Scatter plots of measured and predicted values of phosphorus (P) content. verification effect, the Si-SVR model performs best, with the _RPD_ value being 2.37. Despite the simplicity of the PLS model, its accuracy is relatively poor. The validation determination coefficient _Rc_ value is merely 0.8908, while the _RMSEP_ and _RPD_ are 4.5288% and 1.67, respectively. In terms of validation effect, the Si-SVR model, the SVR model, and the Si-PLS model outperform the PLS model.