## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Leaf Position**} & \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Modeling Accuracy**} & \multicolumn{4}{c}{**Verification Accuracy**} \\ \cline{3-8}  & & \(\mathbf{R^{2}}\) & **RMSE** & **NRMSE** & \(\mathbf{R^{2}}\) & **RMSE** & **NRMSE** \\ \hline \multirow{4}{*}{**Flag leaf**} & PLSR & 0.79 \({}^{**}\) & 0.06 & 0.05 & 0.71 \({}^{**}\) & 0.08 & 0.07 \\  & SVM & 0.66 \({}^{**}\) & 0.07 & 0.14 & 0.65 \({}^{**}\) & 0.05 & 0.10 \\  & RF & 0.74 \({}^{**}\) & 0.06 & 0.04 & 0.65 \({}^{**}\) & 0.09 & 0.08 \\  & MLR & 0.84 \({}^{**}\) & 0.06 & 0.05 & 0.89 \({}^{**}\) & 0.07 & 0.06 \\ \hline \multirow{4}{*}{Upper 1 leaf} & PLSR & 0.76 \({}^{**}\) & 0.10 & 0.06 & 0.79 \({}^{**}\) & 0.11 & 0.06 \\  & SVM & 0.74 \({}^{**}\) & 0.08 & 0.09 & 0.78 & 0.12 & 0.13 \\  & RF & 0.72 \({}^{**}\) & 0.13 & 0.08 & 0.64 \({}^{**}\) & 0.09 & 0.05 \\  & MLR & 0.77 \({}^{**}\) & 0.12 & 0.07 & 0.80 \({}^{**}\) & 0.08 & 0.05 \\ \hline \multirow{4}{*}{Upper 2 leaf} & PLSR & 0.60 \({}^{**}\) & 0.13 & 0.10 & 0.78 \({}^{**}\) & 0.08 & 0.06 \\  & SVM & 0.67 \({}^{**}\) & 0.07 & 0.06 & 0.42 \({}^{**}\) & 0.19 & 0.17 \\  & RF & 0.64 \({}^{**}\) & 0.07 & 0.05 & 0.59 \({}^{*}\) & 0.18 & 0.13 \\  & MLR & 0.73 \({}^{**}\) & 0.12 & 0.09 & 0.74 \({}^{**}\) & 0.09 & 0.07 \\ \hline \multirow{4}{*}{Upper 3 leaf} & PLSR & 0.53 \({}^{**}\) & 0.10 & 0.09 & 0.70 \({}^{**}\) & 0.07 & 0.07 \\  & SVM & 0.52 \({}^{**}\) & 0.09 & 0.15 & 0.50 \({}^{**}\) & 0.08 & 0.14 \\  & RF & 0.54 \({}^{**}\) & 0.09 & 0.08 & 0.47 \({}^{**}\) & 0.11 & 0.10 \\  & MLR & 0.58 \({}^{**}\) & 0.09 & 0.08 & 0.73 \({}^{**}\) & 0.09 & 0.09 \\ \hline \multirow{4}{*}{Upper 4 leaf} & PLSR & 0.33 \({}^{**}\) & 0.10 & 0.13 & 0.55 \({}^{**}\) & 0.07 & 0.09 \\  & SVM & 0.29 \({}^{**}\) & 0.10 & 0.16 & 0.42 \({}^{**}\) & 0.08 & 0.12 \\ \cline{1-1}  & RF & 0.47 \({}^{**}\) & 0.09 & 0.11 & 0.35 \({}^{*}\) & 0.14 & 0.17 \\ \cline{1-1}  & MLR & 0.51 \({}^{**}\) & 0.06 & 0.08 & 0.57 \({}^{**}\) & 0.07 & 0.09 \\ \hline \end{tabular} Note.* \({}^{*}\) indicates a significant correlation at the 0.05 level and \({}^{**}\) indicates a significant correlation at the 0.01 level.  
\end{table} 
Table 7: Model accuracy of estimating nitrogen content based on sensitive wavelet. and all of them obtained high prediction accuracy. Among them, the flag leaf and the upper 1 leaf showed higher prediction accuracy, and MLR and SVM showed better overall prediction performance. The results can be ascribed to the fact that the upper leaves contribute the most to the canopy spectra, and as the leaf position decreases, the corresponding contribution to the canopy spectra gradually decreases. This conclusion is highly consistent with that of [PERSON] [23]. Machine learning has been widely used in the field of remote sensing in recent years, and our study successfully constructed nitrogen content inversion models using four algorithms, all of which achieved high prediction accuracy. However, due to the small amount of sample data used in the paper, the model may be overfitted when modeling using machine learning methods, so further increase in sample data is needed to improve the robustness of the model. The research results can provide reference for other crops in field phenotype research.