## 3 Results
### Evaluation of CNN Models for Accurate Prediction of Chlorophyll a + b Content
The CNN2-4 model recorded an R\({}^{2}\) of 0.919 and an RMSE of 2.259 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.860 and an RMSE of 3.138. The CNN3-1 model had an R\({}^{2}\) of 0.917 and an RMSE of 2.288 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.859 and an RMSE of 3.135. The CNN3-2 model showed an R\({}^{2}\) of 0.917 and an RMSE of 2.288 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.859 and an RMSE of 3.135. The CNN3-2 model recorded an R\({}^{2}\) of 0.916 and an RMSE of 3.203 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.859 and an RMSE of 3.126. The CNN3-3 model recorded an R\({}^{2}\) of 0.916 and an RMSE of 2.303 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.805 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.859 and an RMSE of 3.136. The CNN3-3 model recorded an R\({}^{2}\) of 0.919 and an RMSE of 2.266 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.806 and an RMSE of 3.388. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Cross-**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} \\  & **Validation** & & & & \\ \hline \multirow{3}{*}{CNN1-1} & None & **0.925** & 4.719 & 2.172 & 1.790 \\  & 10-Fold & **0.880** & 9.437 & 2.983 & 2.316 \\  & None & 0.923 & 4.857 & 2.204 & 1.809 \\  & 10-Fold & 0.874 & 9.669 & 3.003 & 2.254 \\  & None & 0.921 & 4.973 & 2.230 & 1.832 \\  & 10-Fold & 0.872 & 9.815 & 3.025 & 2.321 \\  & None & 0.925 & 4.722 & 2.173 & 1.801 \\  & 10-Fold & 0.879 & 9.425 & 2.944 & 2.227 \\  & None & 0.919 & 5.135 & 2.266 & 1.786 \\  & 10-Fold & 0.861 & 10.638 & 3.109 & 2.364 \\  & None & 0.915 & 5.345 & 2.312 & 1.914 \\  & 10-Fold & 0.858 & 10.862 & 3.156 & 2.368 \\  & None & 0.920 & 5.061 & 2.250 & 1.848 \\  & 10-Fold & 0.862 & 10.670 & 3.128 & 2.364 \\  & None & 0.919 & 5.104 & 2.259 & 1.805 \\  & 10-Fold & 0.860 & 10.784 & 3.138 & 2.362 \\  & None & 0.917 & 5.233 & 2.288 & 1.869 \\  & 10-Fold & 0.859 & 10.767 & 3.135 & 2.335 \\  & None & 0.917 & 5.234 & 2.288 & 1.864 \\  & 10-Fold & 0.859 & 10.694 & 3.126 & 2.350 \\  & None & 0.916 & 5.305 & 2.303 & 1.804 \\  & 10-Fold & 0.859 & 10.823 & 3.151 & 2.381 \\  & None & 0.829 & 10.795 & 3.286 & 2.027 \\  & 10-Fold & 0.806 & 14.180 & 3.388 & 2.599 \\ \hline \end{tabular}
\end{table} 
Table 8: Results of different _chlorophyll a + b_ content regression models. The SVR model had an R\({}^{2}\) of 0.131 and an RMSE of 7.407 with 10-fold cross-validation results of R\({}^{2}\) at 0.148 and RMSE at 8.302. The RFR model recorded an R\({}^{2}\) of 0.879 and an RMSE of 2.760 with 10-fold cross-validation R\({}^{2}\) of 0.794 and RMSE of 3.781. The GBR model showed an R\({}^{2}\) of 0.871 and an RMSE of 2.853 with 10-fold cross-validation results of R\({}^{2}\) at 0.813 and RMSE at 3.663. The PLSR model demonstrated an R\({}^{2}\) of 0.771 and an RMSE of 3.805 with 10-fold cross-validation results of R\({}^{2}\) at 0.769 and RMSE of 4.100. Experimental results indicate that compared to traditional regression models, the CNN models developed in this study generally outperformed the conventional methods. Among all the models evaluated, the self-developed CNN1-1 model showed optimal performance, achieving the highest R\({}^{2}\) of 0.925 and maintaining an R\({}^{2}\) of 0.880 even after 10-fold cross-validation. It also exhibited the lowest MSE and RMSE, thereby providing a more accurate prediction of the _chlorophyll a \(+\) b_ content in grape leaves. This superior performance can be attributed to the strong feature learning capability of CNNs, which enables them to effectively capture the complex mapping between spectral information and chlorophyll content. Traditional machine learning models such as SVR, RFR, GBR, and PLSR offer relatively high interpretability and reasonable modeling capabilities in hyperspectral analysis.