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To establish an artificial intelligence (AI)\u2010assisted framework for comprehensive evaluation of tea quality and detailed assessment of the tea consumption experience, this study aims to develop Long\u2010Tea\u2010CLIP (Contrastive Language\u2010Image Pre\u2010training), 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\u201018), soup color (eXtreme Gradient Boosting (XGBoost)), aroma, infused leaf, and taste (multilayer perceptron (MLP)). A deep network derived from ResNet\u201018 integrates dry tea images with seven subdimensions of sensory comments to achieve a refined appearance \u201cgrading.\u201d We apply Tip\u2010CLIP 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\u2010Tea\u2010CLIP trained on 7763 image\u2010text pairs from 38 Longjing tea varieties achieves 92% accuracy, indicating its potential to enhance tea quality control and market transparency.<\/jats:p>","DOI":"10.1002\/advs.202518235","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T13:32:22Z","timestamp":1774618342000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Long\u2010Tea\u2010CLIP: An Expert\u2010Level Multimodal AI Framework for Fine\u2010Grained Green Tea Grading Across Five Sensory Dimensions"],"prefix":"10.1002","volume":"13","author":[{"given":"Yanqun","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Food Science &amp; Technology School of Agriculture and Biology Shanghai Jiao Tong University  Shanghai China"},{"name":"National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients Hunan 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