High-Accuracy Prediction of Chunmee Tea Grade via DeepSpectra Model and Near-Infrared Spectroscopy

Chunmee tea quality is critical to its grading, and accurate identification is essential for quality evaluation and market valuation. However, traditional machine learning relies on manual feature extraction and causes spectral information loss, while conventional one-dimensional convolutional neural networks (1D-CNNs) are restricted by fixed kernels and narrow receptive fields, making multi-scale feature capture difficult. In this study, an improved DeepSpectra model integrated with the Inception module and residual connections was proposed for end-to-end automatic grading of Chunmee tea. A total of 360 samples across six grades (60 samples per grade) were collected using an Antaris II near-infrared spectrometer and preprocessed by multiplicative scatter correction (MSC). The proposed model was compared with other models. Results showed that under a 7:1:2 train-validation-test split, the proposed DeepSpectra achieved an average test accuracy of 96.39 ± 1.63% across ten random sample divisions, significantly outperforming the other models (p < 0.05). The model also exhibited excellent stability in five-fold cross-validation and superior generalization in small-sample scenarios, and a lightweight structure with low inference latency of 2.2 ms, which is suitable for real-time industrial applications. This work provides a reliable, efficient, and end-to-end method for grading Chunmee tea and offers a promising strategy for intelligent and rapid quality control of green tea.