## 3 Results and Discussion
### Discussion  
Based on VIS-NIR spectroscopy, training and testing datasets were established with the original spectral reflectance and 29 pretreatments. These pretreatment methods include Savitzky-Golay (SG), first derivative (FD), second derivative (SD), standard normal variate (SNV), multiplicative scatter correction (MSC), logarithmic transformation (LG), mean center (MC), dislodge tendency (DT), and various combinations of these methods. However, not all pretreatment methods are effective, and SG, SNV, FD, and MSC are used more frequently than the other methods [8; 19; 29; 30]. As a standard preparation of the soil spectral curves, Savitzky-Golay appears in almost every application [30]. In this manuscript, the performance shows that MSC + SD, SG + MSC + SD, SD, SG + SD, SG + SNV + SD, and LG + SD are worse than RS. In particular for SD, only SNV + SD and SG + LG + SD achieved RPD level A, and the best models were entirely without the SD method. The SD method may be seriously disturbed by the features of the VIS-NIR. Therefore, the SD pretreatment method was not useful for the prediction of the VIS-NIR spectrum of the soil-available potassium content. Twenty-three pretreatment methods were better than RS, 10 without SG and 13 with SG. Only three of the methods did not include SG from the best 11 models. Therefore, the SG method has a great influence on the prediction of soil-available potassium content. The frequency of the DT method is most common after SG in the best models, and the DT is a transformation that usually occurs after SNV. Hence, SG + SNV + DT is the best pretreatment method in this study, with most models at RPD level A. The different regression models have linear and nonlinear kernel functions. From Table 6 of this manuscript, the linear regression models are more stabilized, especially PLS_linear, which has the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Number** & **Max/mg:kg\({}^{-1}\)** & **Min/mg:kg\({}^{-1}\)** & **Average/mg:kg\({}^{-1}\)** & **Standard Deviation** \\ \hline Total & 40 & 670 & 300 & 416 & 101.7 \\ Train & 28 & 670 & 300 & 415 & 101.3 \\ Test & 12 & 630 & 300 & 418.3 & 102.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: High concentration of soil-available potassium sample statistics. Figure 9: Scatter plots of best models with different concentrations of samples; (**a**) AdaBoost with low concentration samples; (**b**) Gradient boosted regression trees(GBRT) with high concentration samples. fewest number of models with RPD level D. The sigmoid kernel function is the worst, and the RBF kernel function is better than linear functions. The best models of PLS and SVR are PLS_RBF and SVR_RBF. The accuracy of the regression model with the RBF function reached the best performance. Therefore, the feature of the VIS-NIR follows the normal distribution. The methods of PLS [9; 10; 29; 30] and SVR [17; 18] are widely used for the calibration of VIS-NIR spectra, but boosting regression algorithms are almost never used. GBRT and AdaBoost, which are boosting algorithms, can be effectively calibrated to predict the soil-available potassium content. Boosting is a frame algorithm that produces a predictor in the form of an ensemble of multiple weak predictors. GBRT improves the prediction accuracy by building each decision tree for the past residuals rather than the response variable [21]. The AdaBoost algorithm trains a weak classifier step by step, and every weak classifier is trained on a different weight set of the sample subset. Then, a strong classifier is finally constructed by selecting each training iteration [22]. GBRT calculates the gradient value to locate the deficiency of the model, but AdaBoost is based on loss assessment of the prediction to adjust the weight of the sample subset. From the above figure and table, both algorithms have better performance than other regression algorithms. GBRT and AdaBoost both exhibit the best prediction of soil-available potassium content by VIS-NIR.