## 4 Discussion  
The results highlight the effectiveness of both ResNet1D and CNN1D architectures for our classification problem, with ResNet1D showing slightly better performance in most configurations. The modest performance advantage of ResNet1D suggests that its skip connections contribute positively to feature extraction and classification accuracy in hyperspectral data analysis. Figure 5: Locations of cultivars (provinces in Thailand) predicted to have high flavonoid and phenolic contents. The effectiveness of different feature extraction methods varied significantly in our experiments. Wavelet transformation consistently demonstrated superior performance across both architectures, particularly when used independently. Notably, ResNet1D with wavelet transformation alone achieved the best overall performance (AUC: 0.8246 \(\pm\) 0.0180, ACC: 0.7702 \(\pm\) 0.0188), surpassing even the combined feature approach. When used independently, Gabor-like filtering showed notably lower performance compared to wavelet transformation in both architectures. Combining Gabor-like filtering with wavelet transformation led to slightly decreased performance compared to using wavelet transformation alone. This suggests that Gabor-like filtering features might introduce noise that slightly impairs the model's discriminative capabilities when combined with wavelet features. Additionally, the superior performance of the wavelet transformation over Gabor-like filtering may be due to its ability to capture both global and local spectral features through multiresolution analysis using the Discrete Wavelet Transform (DWT) with Daubechies 4 (db4). Unlike Gabor filters, which reduce each filtered signal to a single value and risk losing important details, the wavelet method preserves a richer set of features across multiple frequency levels. This flexibility allows wavelets to better capture non-stationary and multiscale patterns typical in hyperspectral plant data. The importance of feature extraction methods was further demonstrated in experiments without any feature extraction, where both architectures showed substantially lower performance. ResNet1D without feature extraction achieved an AUC of 0.6484 \(\pm\) 0.0353 and ACC of 0.6369 \(\pm\) 0.0388, while CNN1D showed similar results, with an AUC of 0.6555 \(\pm\) 0.0187 and ACC of 0.6510 \(\pm\) 0.0518. The application of RFECV showed varying effects depending on the feature extraction method used. While RFECV improved performance when applied to combined features or hyperspectral data without feature extraction, it slightly decreased performance when applied to wavelet features alone. This suggests that wavelet transformation may already provide an optimal feature set, and additional feature selection could potentially remove informative features. These findings indicate that, while both architectures are effective for our classification task, the choice of feature extraction method plays a more crucial role in determining overall performance than the choice of network architecture. Wavelet transformation emerges as the most effective single method for feature extraction in our classification framework. Notably, our experiments revealed that classifying flavonoid content levels using hyperspectral data presents greater challenges compared to phenolic content classification in previous studies [33]. Using the same traditional statistical features (22 features) with neural networks that were effective for phenolic content classification, we achieved an AUC of 0.7603 \(\pm\) 0.0187 and accuracy of 0.7201 \(\pm\) 0.0281 for flavonoid content classification. These results are lower than the performance achieved using our proposed feature extraction methods with ResNet1D and CNN1D architectures. This improvement in performance demonstrates the superiority of our proposed approach combining deep learning architectures with advanced feature extraction methods over traditional statistical feature-based approaches for complex flavonoid content classification tasks.