## 4 Discussion  
This study demonstrates the feasibility and effectiveness of integrating HSI with CNNs for predicting _chlorophyll_\(a+b\) content in grapevine leaves. By employing the VGG16-U-Net model for hyperspectral image segmentation, the approach enables the automatic extraction of leaf regions and reduces background noise interference--offering a distinct advantage over many traditional spectral studies that rely on manual region selection.  
For spectral preprocessing, spectral preprocessing methods such as FFT, SNV + FD, MSC + FD, SNV + FFT, and MSC + FFT significantly enhanced the prediction accuracy of _chlorophyll_\(a+b\) content. However, some methods, such as SNV, MSC, SD, and SNV + SD, actually reduced model performance. The proper choice of preprocessing is difficult to assess prior to model validation [43]. Although previous studies have predominantly employed common preprocessing methods such as SNV or FD, our findings reveal that FFT preprocessing--despite being less commonly used--produced the most effective results. This suggests that frequency-domain transformations can effectively capture periodic or structural patterns in the spectral data that are associated with chlorophyll concentration. Such information may not be readily apparent in the original or derivative spectra, highlighting the potential of FFT as a valuable preprocessing technique for spectral regression tasks.  
Figure 8: Relationship between predicted and measured _chlorophyll_\(a+b\) content.  
Interestingly, the shallow CNN architecture (CNN1-1) outperformed deeper and more complex models, indicating that increased network depth or complexity does not necessarily lead to improved performance--especially when feature extraction is already supported by appropriate preprocessing and ROI segmentation. This finding may be attributed to the relatively small sample size in this study, where deeper networks are more prone to overfitting.  
To further evaluate the performance of the proposed VGG16-U-Net-FFT-CNN1-1 framework for chlorophyll content prediction, we compared its results with those reported in previous studies that also utilized hyperspectral imaging for chlorophyll estimation. As shown in Table 9, the proposed VGG16-U-Net-FFT-CNN1-1 framework outperformed the models developed by [PERSON] et al. [15], [PERSON] et al. [16], and [PERSON] et al. [17], offering a novel and more effective approach to chlorophyll content prediction.  
From a practical perspective, this approach offers a non-destructive, efficient, and scalable solution for monitoring plant physiological traits. Within the context of precision agriculture, these models could be embedded into field-deployable platforms to support rapid chlorophyll diagnostics, facilitate nutrient management, and enable early stress detection. However, this study was based on hyperspectral data collected at a single time point. Future work will focus on expanding the dataset to encompass the entire growth cycle of grapevines, enabling the model to be generalized across different developmental stages. Additionally, this research will not be limited to chlorophyll content but will also focus on other plant physiological and biochemical indicators--such as nitrogen content, leaf water status, and disease markers--in order to establish a more comprehensive and integrated crop monitoring framework. In addition, future research will consider other crops of higher economic value and scientific significance.