## 3 Results and Analysis
### Effective Wavelengths Selection  
Considering the water content of the selected soil sample, which is not a horizontal value, and the impact on the spectrum by the vibration multiple of O-H, N-H, and C-H groups in the soil [29], partial least squares (PLS) regression algorithm and support vector regression (SVR) algorithm were adopted in this study for modeling. Given the aim of this study, to build an effective prediction model for main soil nutrients, attention should be paid to timeliness for the processing of spectral data. Due to different variables that will affect the stability of the model, the redundant information of the input model can be minimized by the combination of effective wavelength selection and regression algorithm in the experimental process. Therefore, the joint interval method in the effective wavelength selection method was adopted in this study to screen variables, extract the information contained in the specific interval related to soil nutrients as the input information of the model, and establish the main soil nutrient content prediction model, which is effective in significantly reducing the redundant information between variables and improving the efficiency and accuracy of operation for the model. Below are the main steps taken towards the selection of effective bands of spectral information for three main soil nutrients: N, P, and K.  
Before the prediction model of soil nitrogen content is constructed, it is necessary to select the full wavelength spectral information of the original spectral data. To begin with, the full spectral wavelength is divided into 10, 11, 12,..., 30 subintervals with equal width. The impact caused by the number of sub spectral regions on the prediction performance of the sample model was explored. If the best effect can be achieved in the 30 th subinterval, it is necessary to continue dividing the full spectrum wavelength into 31, 32,..., 40 subintervals. PLS prediction models were established by combining two, three, and four subintervals, respectively. Herein, the range of efficiency wavelength selection for SVR is consistent with that for PLS. The dependent variable set and independent variable set corresponding to the joint subinterval are expressed as  
\[F_{0}= \begin{bmatrix}y_{11}&\cdots&y_{1p}\\ \cdots&\cdots&\cdots\\ y_{n1}&\cdots&y_{np}\end{bmatrix} ,E_{0}= \begin{bmatrix}X_{11}&\cdots&X_{1m}\\ \cdots&\cdots&\cdots\\ X_{n1}&\cdots&X_{nm}\end{bmatrix}  \tag{4}\]  
where \(F_{0}\) represents the dependent variable and \(E_{0}\) refers to the randomly selected subinterval. The accuracy measurement standard applied to the modeling of a certain characteristic interval is purposed to leave a cross-validation root mean square error of calibration (\(V_{\text{RMSEC}}\)). The calculation formula is expressed as  
\[V_{\text{RESEC}}=\sqrt{\frac{\sum\limits_{i=1}^{n}(\hat{y}_{i}-y_{i})}{n-1}} \tag{5}\]  
where \(\hat{y}_{i}\) represents the estimated value of the \(i\)th sample, \(y_{i}\) denotes the actual value of the \(i\)th sample, and \(n\) refers to the number of samples in the calibration sample set. By comparing the \(V_{\text{RMSEC}}\) values of N, P, and K in each subinterval, the optimal subinterval set can be selected to establish the PLS model. The smaller the \(V_{\text{RMSEC}}\) value of each subinterval model, the better the effect produced by the interval model. Therefore, the subinterval with the smallest \(V_{\text{RMSEC}}\) was selected. The basic process of how the algorithm works is as follows:1. The global PLS model of the sample to be tested is established within the full spectrum range, with the \(V_{\text{RMSEC}}\) values of N, P, and K calculated by using formula (5) as the threshold \(V_{\text{RMSEC}}\) for feature interval screening \(V_{\text{RMSEC}}\)-N, \(V_{\text{RMSEC}}\)-P, and \(V_{\text{RMSEC}}\)-K.
2.