The contents of secondary metabolites such as tea polyphenols, amino acids, caffeine, and volatile metabolites in fresh tea leaves are key factors determining the unique flavor and health attributes of finished tea products. However, differences in varieties, cultivation practices, and environmental conditions often lead to variations in these metabolites among fresh tea leaves, thereby affecting tea quality. In order to clarify the various internal and external factors that influence the formation of the quality of fresh tea leaves and their mechanism of action. This article mainly reviews the research on fresh leaf quality in the past decade. Firstly, it clarifies the molecular basis of metabolic differences among varieties. Then, it summarizes the regulatory mechanisms of underground (soil, microorganisms) and above-ground (light, temperature, humidity) environments on key metabolic pathways, and focuses on evaluating the effects of intercropping, fertilization, and other cultivation measures on improving tea quality. This review found that the specific gene expression of varieties, the transmission of environmental signals, and cultivation interventions jointly drive the synthesis and accumulation of tea polyphenols, amino acids, caffeine, and aroma substances. However, no one has ever systematically reviewed it. Therefore, it provides certain theoretical references for improving the quality of fresh leaves.

Catechins are pivotal determinants of tea quality, with soil environmental factors playing a crucial role in the synthesis and accumulation of these compounds. To investigate the impact of changes in tea garden soil environments on the catechin content in sun-dried tea, this study measured the catechin content in soil samples and corresponding tea leaves from Nanhua, Yunnan, China. By integrating the variations in catechin content with those of 17 soil factors and employing COX regression factor analysis, it was found that pH, organic matter (OM), fluoride, arsenic (As), and chromium (Cr) were significantly correlated with catechin content (p < 0.05). Further, using the LASSO regression for variable selection, a model named LCLN-CA was constructed with four variables including pH, OM, fluoride, and As. The LCLN-CA model demonstrated high fitting accuracy with AUC values of 0.674, 0.784, and 0.749 for catechin content intervals of CA ≤ 10%, 10% < CA ≤ 20%, and 20% < CA ≤ 30% in the training set, respectively. The validation set showed AUC values of 0.630, 0.756, and 0.723, respectively, indicating a well-calibrated curve. Based on the LCLN-CA model and the DynNom framework, a visual prediction system for catechin content in Yunnan sun-dried tea was developed. External validation with a test dataset achieved an $Accuracy$ of 0.870. This study explored the relationship between soil-related factors and variations in catechin content, paving a new way for the prediction of catechin content in tea and enhancing the practical application value of artificial intelligence technology in agricultural production.