## 3 Results and Discussion
### Performance of Regression Models with Different Pretreatment Methods
Therefore, it is significant to choose adaptive pretreatment and regression algorithms to predict the content of soil-available potassium by VIS-NIR. In Table 4, the RPD level of the regression models is compared to determine the influence of the different pretreatment methods based on the testing dataset. Level A denotes that the regression model has the best stability, and level D means the worst. The RPD using the RS dataset as a baseline shows that a level A model is difficult to achieve without pretreatment. SVR_sigmoid, GBRT, and AdaBoost on the RS dataset are level D, but the PLS algorithm with linear and nonlinear functions was level B. The regression of the RBF kernel function using partial pretreatment methods could achieve level A, but the model with linear and sigmoid kernel functions could not attain level A, even if all pretreatment methods were used. In conclusion, regression algorithms and pretreatment methods are significant for the prediction of the VIS-NIR of soil-available potassium, as shown in Table 4. Table 4 reveals that the prediction performance of different pretreatment methods at different levels. Through the statistics, Figure 5 shows the visualization of the RPD level of models with 29 pretreatment methods and RS in order. The black areas represent level D, the grey areas represent level C, the yellow areas represent level B, and the orange areas represent level A. There are six pretreatment methods, and RS could not achieve level A with any regression algorithms. Only the SG + SNV + DT method has four models with RPD level A, and SG + DT and SG + SNV not only have two models with RPD level A and five models with RPD level B but also have only one model with RPD level C and zero models with RPD level D. Therefore, SG + DT, SG + SNV, and SG + SNV + DT are the most stable pretreatment methods for the prediction of soil-available potassium with regression algorithms. Table 5 shows the number of models with different RPD levels. The statistics show that the number of PLS_RBFs with level A is the highest, but the boosting algorithms represent more stability because GBRT and AdaBoost show the highest numbers of models with level A and level B, showing that these levels were both meaningful for predicting the content of soil-available potassium. Therefore, the boosting algorithms are better than PLS and SVR with linear and nonlinear functions. Figure 5 and Table 5 indicate that there are few models at level A; therefore, other evaluation metrics are needed. The RPIQ value was computed as an important evaluation metric and is presented in Figure 6 because the content of soil-available potassium has a biased normal distribution. The green \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**RPD** & **PLS\_Linear** & **PLS\_Sigmoid** & **PLS\_RBF** & **SVR\_Linear** & **SVR\_Sigmoid** & **SVR\_RBF** & **GBRT** & **AdaBoost** \\ \hline Level A & 0 & 0 & 12 & 0 & 0 & 4 & 11 & 11 \\ Level B & 17 & 18 & 6 & 3 & 2 & 10 & 13 & 13 \\ Level C & 10 & 1 & 0 & 9 & 12 & 1 & 0 & 0 \\ Level D & 3 & 11 & 12 & 18 & 16 & 15 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number of different models at varying RDP levels. color represents the RPIQ values of GBRT and AdaBoost on the testing dataset. Figure 6 shows that most of the pretreatment datasets exhibited improved performance when boosting regression algorithms were used for prediction of the content of soil-available potassium. Dark green indicates the RPIQ values for AdaBoost with all pretreatment methods. AdaBoost performed extremely well with SNV + FD, LG + FD, SG + FD, SG + SNV + FD, and SG + LG + FD because the RPIQ values were greater than 3. Additional methods all had values less than 3, except for GBRT with DF. The RPIQ value of SG + LG + FD is the maximum for AdaBoost. As a result, the next part is the comparison and analysis of the best regression models.