## 5 Conclusions  
This study demonstrates the effectiveness of deep learning approaches combined with feature extraction methods for classifying flavonoid content levels in holy basil using hyperspectral imaging data. Our findings reveal that ResNet1D architecture generally outperforms CNN1D, highlighting the advantages of skip connections for complex spectral data analysis. The evaluation of different feature extraction methods showed that wavelet transformation consistently provided superior performance across architectures, while Gabor-like filtering exhibited lower performance when used independently. Notably, the combination of both methods did not improve performance compared to wavelet transformation alone, suggesting that wavelet features may be optimal for this classification task. The investigation of RFECV revealed varying effectiveness, depending on the feature extraction method employed. 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 for classification, and further feature selection could potentially remove informative features. Our models successfully identified Thai holy basil (_O. tenuiformum_ L) samples with consistently high levels of flavonoid contents, achieving high reliability scores (\(\geq\)0.95). From the analysis of 113 samples representing 18 different cultivars, we identified 18 promising samples with normal-to-high levels of both phenolic and flavonoid contents from diverse geographical locations across Thailand. The prevalence of Cut 3 samples among highlighting varieties indicates that harvest maturity significantly influences phytochemical content. Both commercial varieties, particularly from Chia Tai Co. Ltd., and local varieties, notably from Chai Badan, Loppuri, demonstrated excellent potential for high phytochemicalal accumulation. These findings provide valuable insights for future applications of hyperspectral imaging in agriculture, particularly for the non-destructive assessment of bioactive compounds. The identification of superior cultivars across different regions of Thailand highlights the importance of preserving local genetic diversity in crop improvement efforts. These results also suggest practical implications for agricultural practices, including the optimisation of harvest timing to maximize flavonoid yield and the selection of cultivars with high phytochemical content for cultivation. Moreover, the integration of hyperspectral imaging with deep learning presents a promising approach for non-destructive, in-field screening to support quality monitoring and decision-making in breeding programs. Future research could explore additional feature extraction techniques and their interactions with various deep learning architectures to further enhance classification performance, ultimately contributing to more effective breeding programs for developing Thai holy basil varieties with enhanced phytochemical profiles. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15137582/s1](https://www.mdpi.com/article/10.3390/app15137582/s1)]([https://www.mdpi.com/article/10.3390/app15137582/s1](https://www.mdpi.com/article/10.3390/app15137582/s1)). Table S1. A complete list of samples with prediction scores. Conceptualization, A.S., P.C. and K.P. ; formal analysis, A.S.; methodology, A.S. and K.P. ; validation, A.S. and K.P. ; data curation, P.C. ; resources, A.S. and K.P. ; investigation, A.S., P.C. and K.P. ; writing--original draft preparation, A.S.; writing--review and editing, A.S., P.C. and K.P. ; visualization, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript. This research budget was allocated by National Science, Research and Innovation Fund (NSRF), and King Mongkut's University of Technology North Bangkok (Project no. KMUTNB-FF-67-B-24).