## 1 Introduction
This technology combines the power of spectroscopy with imaging capabilities, allowing for the collection and processing of information from across the electromagnetic spectrum [26; 27]. Hyperspectral imaging generates large datasets with high spectral and spatial resolution, providing detailed information about the chemical composition of plant tissues while preserving spatial context [28]. Hyperspectral imaging combines spectroscopy and imaging to collect and process information from across the electromagnetic spectrum. This technology generates large datasets with high spectral and spatial resolution, providing detailed information about the chemical composition of plant tissues. However, the high dimensionality and complexity of hyperspectral data pose significant challenges for traditional data analysis methods. Machine learning methods have been famous for various applications [29; 30] including in plant sciences [31; 32]. Recently, [PERSON] et al. [33] quantified phenolic contents using hyperspectral imaging of Thai holy basil and machine learning to non-destructively assess phenolic content levels. They employed 22 statistical features extracted from hyperspectral data as inputs for classification using a neural network. The model achieved strong predictive accuracy, with a high value of area under the ROC curve for phenolic level classification. This approach offers a rapid alternative to traditional methods for antioxidant analysis and demonstrates the potential of integrating hyperspectral imaging and machine learning in plant phenotyping for phytochemical assessments. In addition to traditional machine learning algorithms, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable success in analysing complex spectral data [34; 35]. Among these, one-dimensional CNNs, such as one-dimensional residual neural networks (ResNets), have demonstrated particular efficacy in processing spectral signatures [36; 37]. ResNet1D, an adaptation of the ResNet architecture for one-dimensional data, offers advantages in handling the sequential nature of spectral information while mitigating the vanishing gradient problem through residual connections. This study aims to develop and validate a novel analysis framework for quantifying flavonoid content in basil using hyperspectral imaging coupled with ResNet1D analysis. By utilizing the power of deep learning and non-destructive imaging techniques, we seek to establish a rapid, accurate, and scalable method for assessing the nutritional and potential therapeutic value of basil. This approach has the potential to revolutionize quality control processes in the herb industry and facilitate more efficient screening of basil varieties for enhanced flavonoid content. In the subsequent sections, we detail our comprehensive methodology encompassing holy basil cultivation, flavonoid content extraction, hyperspectral data acquisition, preprocessing protocols, feature transformation, and machine learning framework development, followed by an evaluation of deep learning models' performance in predicting flavonoid content from hyperspectral data. The experimental design and validation strategies are thoroughly described to ensure reproducibility. We further analyse the implications of our findings for characterizing and differentiating Thai holy basil cultivars. Additionally, we discuss the key findings and implications of our integrated deep learning and hyperspectral imaging approach, providing insights for future improvements in non-destructive phytochemical analysis of medicinal herbs.