## 5 Conclusions  
This study proposed a data-driven integrated framework that combines HSI technology and CNNs for the rapid, accurate, and efficient prediction of _chlorophyll a + b_ content in grape leaves, using hyperspectral images and corresponding _chlorophyll a + b_ content data as inputs.  
Initially, the self-developed CNN1-1 model constructed from the original average spectra achieved an R\({}^{2}\) of 0.867 and an RMSE of 2.899. The VGG16-U-Net model was then used to segment the hyperspectral images, resulting in an improved R\({}^{2}\) of 0.904 and a reduced RMSE of 2.465. Subsequently, the best spectral preprocessing method, FFT, was selected from 15 options to construct CNN1-1 model on the average spectra of ROIs, leading to further enhancement in R\({}^{2}\). Compared to a series of traditional regression models such as SVR, RFR, GBR, and PLSR, CNN1-1 among the self-developed CNN models demonstrated superior performance. The proposed VGG16-U-Net-FFT-CNN1-1 framework achieved an R\({}^{2}\) of 0.925 and an RMSE of 2.172, indicating its strong potential for accurate chlorophyll content estimation in grape leaves. After 10-fold cross-validation, the R\({}^{2}\) and RMSE were  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **MSE** & **RMSE** & **MAE** \\ \hline VGG16-U-Net-FFT-CNN1-1 & **0.925** & 4.719 & 2.172 & 1.790 \\ [PERSON] et al. [15] & 0.746 & - & 2.018 & - \\ [PERSON] et al. [16] & 0.61 & - & 4.439 & - \\ [PERSON] et al. [17] & 0.839 & - & 1.451 & - \\ \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 9: Comparison between the proposed VGG16-U-Net-FFT-CNN1-1 model and other chlorophyll prediction models.  
0.880 and 2.983, respectively, indicating that the _chlorophyll_\(a+b\) regression model combining HSI technology with CNN produced commendable results. The \(t\)-test and \(F\)-test results further confirm the statistical robustness of the VGG16-U-Net-FFT-CNN1-1 framework. Future research will consider increasing the sample size and spectral variability to further enhance the model's robustness.  
Conceptualization, L.S. and J.X.; methodology, M.Z.; software, M.Z.; validation, L.W. and J.X.; formal analysis, X.Z.; investigation, X.Z.; resources, X.Z.; data curation, M.Z. and J.X.; writing--original draft preparation, M.Z.; writing--review and editing, M.Z.; visualization, L.W.; supervision, L.S.; project administration, L.S.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
1D & one-dimensional \\ CNN & convolutional neural network \\ FD & first derivative \\ FFT & fast Fourier transform \\ GB & gradient boosting \\ GBR & gradient boosting regression \\ HPLC & high-performance liquid chromatography \\ HSI & hyperspectral imaging \\ MAE & mean absolute error \\ MIoU & mean intersection over union \\ MPA & mean pixel accuracy \\ MSC & multiplicative scatter correction \\ MSE & mean square error \\ NDVI & normalized difference vegetation index \\ PLSR & partial least squares regression \\ R\({}^{2}\) & coefficient of determination \\ ReLU & rectified linear unit \\ RF & random forest \\ RFR & random forest regression \\ RGB & red, green, blue \\ RMSE & root mean square error \\ ROI & region of interest \\ RPD & residual predictive deviation \\ SD & second derivative \\ SEP & standard error of prediction \\ SG & Savitzky-Golay \\ SNV & standard normal variate \\ SVR & support vector regression \\ \end{tabular}