## 3 Results
### Evaluation of CNN Models for Accurate Prediction of Chlorophyll a + b Content  
To establish regression models for predicting the _chlorophyll a + b_ content of 204 grape leaf hyperspectral images, the dataset was divided into a training set and a test set in an 8:2 ratio. Using the average spectra of the grape leaves obtained after image segmentation \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Pretreatment** & **Model** &
\begin{tabular}{c} **Cross-** \\ **Validation** \\ \end{tabular} & **R\({}^{2}\)** & **MSE** & **RMSE** & **MAE** \\ \hline None & CNN1-1 & None & 0.904 & 6.077 & 2.465 & 1.980 \\  & & 10-Fold & 0.847 & 11.658 & 3.290 & 2.488 \\ SNV & CNN1-1 & None & 0.902 & 6.168 & 2.484 & 2.001 \\  & & 10-Fold & 0.860 & 10.922 & 3.128 & 2.299 \\ MSC & CNN1-1 & None & 0.904 & 6.062 & 2.462 & 1.938 \\  & & 10-Fold & 0.844 & 11.971 & 3.304 & 2.413 \\ FFT & CNN1-1 & None & **0.925** & 4.719 & 2.172 & 1.790 \\  & & 10-Fold & **0.880** & 9.437 & 2.983 & 2.316 \\ FD & CNN1-1 & None & 0.912 & 5.526 & 2.351 & 1.902 \\  & & 10-Fold & 0.861 & 10.606 & 3.122 & 2.378 \\ SD & CNN1-1 & None & 0.859 & 8.886 & 2.981 & 2.276 \\  & & 10-Fold & 0.843 & 11.829 & 3.307 & 2.434 \\ SNV + FFT & CNN1-1 & None & **0.916** & 5.291 & 2.300 & 1.919 \\  & & 10-Fold & **0.869** & 10.272 & 3.070 & 2.286 \\ SNV + FD & CNN1-1 & None & **0.917** & 5.251 & 2.291 & 1.761 \\  & & 10-Fold & **0.870** & 10.160 & 3.069 & 2.236 \\ SNV + SD & CNN1-1 & None & 0.854 & 9.236 & 3.039 & 2.177 \\  & & 10-Fold & 0.844 & 11.836 & 3.280 & 2.332 \\ MSC + FFT & CNN1-1 & None & **0.913** & 5.478 & 2.341 & 1.879 \\ MSC + FD & CNN1-1 & 10-Fold & **0.866** & 10.359 & 3.059 & 2.260 \\  & & 10-Fold & **0.869** & 10.240 & 3.089 & 2.237 \\  & & 10-Fold & 0.876 & 7.804 & 2.794 & 2.043 \\ MSC + SD & CNN1-1 & 10-Fold & 0.851 & 11.445 & 3.236 & 2.286 \\ FFT + FD & CNN1-1 & None & 0.870 & 82.24 & 2.868 & 2.086 \\  & & 10-Fold & 0.846 & 11.669 & 3.263 & 2.426 \\ FFT + SD & CNN1-1 & None & 0.822 & 11.231 & 3.351 & 2.365 \\  & & 10-Fold & 0.813 & 13.727 & 3.539 & 2.613 \\ FD + FFT & CNN1-1 & None & 0.896 & 6.562 & 2.562 & 1.954 \\  & & 10-Fold & 0.846 & 11.506 & 3.276 & 2.411 \\  & & None & 0.850 & 9.478 & 3.079 & 2.185 \\ SD + FFT & CNN1-1 & 10-Fold & 0.843 & 11.851 & 3.303 & 2.346 \\ \hline \hline \end{tabular} Note: Results are rounded to three decimal places, and numbers in bold highlight the models with relatively better results.  
\end{table} 
Table 7: Results of the _chlorophyll a + b_ content regression models using different preprocessing methods. and FFT spectral preprocessing, traditional regression models--including SVR, RFR, GBR, and PLRR--as well as 12 CNN models were built to predict the _chlorophyll a + b_ content of grape leaves. The regression models were evaluated using various performance metrics. Table 8 displays the results of the 12 self-developed CNN models compared to the four traditional regression models. The CNN1-1 model achieved an R\({}^{2}\) of 0.925 and an RMSE of 2.172 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.880 and an RMSE of 2.983. The R\({}^{2}\) of the CNN1-2 model was 0.923 with an RMSE of 2.204, and after 10-fold cross-validation, the R\({}^{2}\) was 0.874 and the RMSE was 3.003. The CNN1-3 model had an R\({}^{2}\) of 0.921 and an RMSE of 2.230 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.872 and an RMSE of 3.025. The CNN1-4 model achieved an R\({}^{2}\) of 0.925 and an RMSE of 2.173 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.879 and an RMSE of 2.944. The CNN2-1 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 0.861 and an RMSE of 3.109. The CNN2-2 model had an R\({}^{2}\) of 0.915 and an RMSE of 2.312 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.858 and an RMSE of 3.156. The CNN2-3 model exhibited an R\({}^{2}\) of 0.920 and an RMSE of 2.250 with 10-fold cross-validation results showing an R\({}^{2}\) of 0.862 and an RMSE of 3.128.