## 3 Results and Discussion
### Spectral Features and Preprocessing  
The raw spectral data of all samples are shown in Figure 8a. Figure 8a shows that the degree of response of the spectral curves of corn with different leaf nitrogen contents varied. Still, the overall trend of changes showed the same regularity. Corn is sensitive to changes in nitrogen content, and leaves' spectral absorption characteristics are mainly influenced by various pigments and cellular tissue structures in the wavelength region [30]. Due to background noise, scattered light, and baseline drift during the acquisition process, there was a certain amount of noise in the spectral profile. To screen the optimal preprocessing method, a quantitative analysis of each preprocessing method was performed, and the preprocessing was modeled and analyzed using the partial least squares method. It is shown in Table 3 that the first-order derivative preprocessing had the best processing effect with wavelength range from 400 to 1000 nm, the R\({}_{\text{P}}\)\({}^{2}\) of the PLSR model was 0.84, and the RMSEP was 0.17. As seen in Figure 8b, the spectra after the first-order derivative processing eliminated the interference of the baseline and background and improved the spectral resolution. The selection of spectrally sensitive variables affects the results and performance of the model, with the ultimate goal of selecting the few combinations of characteristic wavelengths from the original bands that are informative, less correlated, and have good class separability [31]. A joint algorithm of CARS and SPA was used for the pre-processed spectral data to filter the sensitive wavelengths from the full spectrum of 224 variables. CARS sets the number of Monte Carlo sampling runs to 50, while tenfold cross-validation is used to evaluate the effect of each subset, and the spectral feature variables are selected for the first-order derivative preprocessed data, as shown in Figure 9. From Figure 9a, it can be seen that the number of variables decreases rapidly in 25 \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Sample** & **Max** & **Min** & **Mean** & **SD** \\ \hline Calibration dataset & 64 & 2.57 & 0.65 & 1.63 & 0.43 \\ \hline Validation dataset & 18 & 2.69 & 1.12 & 1.69 & 0.40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical analysis of LNC data. Figure 8: (**a**) Original spectral curves; (**b**) first derivative curves. exponential decay function EDP, and then gradually slows down and stabilizes, which indicates that the CARS algorithm has \"coarse selection\" and \"selection\" in the feature variable selection. This process indicates that the CARS algorithm has two processes: \"coarse selection\" and \"selection\" in feature selection. As can be seen in Figure 9b, in the initial stage, the ten-fold cross-validation RMSECV values of the individual PLS models became progressively smaller as the number of iterations increased due to the exclusion of a large number of variables that were not relevant to the prediction of nitrogen content in corn leaves. When the RMSECV reached the minimum value, the corresponding number of sampling was 24. With a further increase in the number of sampling, the RMSECV also gradually increased, indicating that some crucial variables in the spectrum were eliminated. Therefore, it can be seen in Figure 9c that when the variables obtained in 24 iterations are identified as the characteristic variables for predicting the nitrogen content of corn leaves, a total of 24 variables. Based on the CARS screening wavelengths, another feature variable screening was performed using the SPA algorithm. The SPA algorithm screened the 14 reflected wavelengths with the lowest covariance of leaf nitrogen content as feature wavelengths. The screened wavelengths were correlated with maize LNC as shown in Figure 10. The results showed that the wavelengths at 505.61 nm, 572.1 nm, and 732.01 nm showed a weak correlation with LNC, while the other wavelengths showed a strong correlation.