## 3 Results
### Impact of Different Preprocessing Methods on Regression Models  
Before establishing regression models to predict the _chlorophyll a + b_ content, this study applied 15 distinct preprocessing methods to the average spectra of the segmented  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Segmentation**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Cross-**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} \\  & & & & & & \\ \hline \multirow{2}{*}{Before} & \multirow{2}{*}{CNN1-1} & None & 0.867 & 8.405 & 2.899 & 2.207 \\  & & 10-Fold & 0.822 & 13.539 & 3.531 & 2.699 \\ \multirow{2}{*}{After} & \multirow{2}{*}{CNN1-1} & None & 0.904 & 6.077 & 2.465 & 1.980 \\  & & 10-Fold & 0.847 & 11.658 & 3.290 & 2.488 \\ \hline \hline \end{tabular} Note: Results are rounded to three decimal places.  
\end{table}
Table 6: Results of the _chlorophyll a + b_ content regression models using hyperspectral images before and after segmentation.  
Figure 6: (**a**) True RGB image; (**b**) RGB image calculated from the hyperspectral image; (**c**) example of data annotation; (**d**) hyperspectral image at a specific band; (**e**) masked hyperspectral image at a specific band.  
grape leaf hyperspectral images. These methods include SNV, MSC, FFT, FD, SD, and combinations thereof such as SNV + FFT, SNV + FD, SNV + SD, MSC + FFT, MSC + FD, MSC + SD, FFT + FD, FFT + SD, FD + FFT, and SD + FFT. The objective was to explore the impact of spectral preprocessing on the performance of regression models. Figure 7 presents the comparative spectral profiles of grape leaves, showing both the original mean reflectance spectra and their transformed counterparts after spectral preprocessing.  
Figure 7: _Cont._  
_Chlorophyll \(a+b\)_ content predictions were performed using the self-developed CNN1-1 model with the results displayed in Table 7. Among the 15 preprocessing methods, the top five performing were FFT, SNV + FD, MSC + FD, SNV + FFT, and MSC + FFT. Following  
Figure 7: The original mean reflectance spectra and the mean transformed counterparts after spectral preprocessing. (**a**) Original spectra; (**b**) SNV; (**c**) MSC; (**d**) FFT; (**e**) FD; (**f**) SD; (**g**) SNV + FFT; (**h**) SNV + FD; (**i**) SNV + SD; (**j**) MSC + FFT; (**k**) MSC + FD; (**l**) MSC + SD; (**m**) FFT + FD; (**n**) FFT + SD; (**o**) FD + FFT; (**p**) SD + FFT.  
spectral preprocessing, there was a noticeable improvement in R\({}^{2}\) and a reduction in RMSE both before and after 10-fold cross-validation. The FFT preprocessing method improved R\({}^{2}\) from 0.904 to 0.925 and reduced RMSE from 2.465 to 2.172 with 10-fold cross-validation further improving R\({}^{2}\) to 0.880 and reducing RMSE to 2.983. The SNV + FD combination achieved an R\({}^{2}\) of 0.917 and an RMSE of 2.291 with cross-validation results of R\({}^{2}\) at 0.870 and RMSE at 3.069. The MSC + FD combination resulted in an R\({}^{2}\) of 0.917 and an RMSE of 2.294 with cross-validation results of R\({}^{2}\) at 0.869 and RMSE at 3.089. The SNV + FFT combination reached an R\({}^{2}\) of 0.916 and an RMSE of 2.300 with a post cross-validation R\({}^{2}\) of 0.869 and RMSE of 3.070. Lastly, the MSC + FFT combination showed an R\({}^{2}\) of 0.913 and an RMSE of 2.341 with cross-validation results of R\({}^{2}\) at 0.866 and RMSE at 3.059.  
Among these methods, FFT preprocessing yielded the best outcomes, attaining the highest pre- and post-cross-validation R\({}^{2}\) values of 0.925 and 0.880, respectively. The efficacy of the FFT spectral preprocessing method was validated, making it the chosen uniform preprocessing method for regression model comparisons in the subsequent sections.