Long-Tea-CLIP: An Expert-Level Multimodal AI Framework for Fine-Grained Green Tea Grading Across Five Sensory Dimensions.

Traditional tea quality evaluation depends on human evaluators, limiting scalability, and consistency. To establish an artificial intelligence (AI)-assisted framework for comprehensive evaluation of tea quality and detailed assessment of the tea consumption experience, this study aims to develop Long-Tea-CLIP (Contrastive Language-Image Pre-training), a multimodal tea grading system that combines computer vision and chemoinformatics. It integrates five sensory evaluation dimensions for green tea, using separate submodels for appearance (ResNet-18), soup color (eXtreme Gradient Boosting (XGBoost)), aroma, infused leaf, and taste (multilayer perceptron (MLP)). A deep network derived from ResNet-18 integrates dry tea images with seven subdimensions of sensory comments to achieve a refined appearance "grading." We apply Tip-CLIP supervised MLP on feature data extraction from infused leaf and chemical data of aroma and taste to enhance accuracy. Submodel outputs are weighted into a unified framework to produce an overall score. Long-Tea-CLIP trained on 7763 image-text pairs from 38 Longjing tea varieties achieves 92% accuracy, indicating its potential to enhance tea quality control and market transparency.