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.

To explore the effects of different withering methods on the quality of Congou black tea, this study focused on five different withering methods: natural withering, warm-air withering, sun–natural combined withering, sun withering, and shaking withering. Gas chromatography‒mass spectrometry (GC‒MS), high-performance liquid chromatography (HPLC), and ion-exchange chromatography techniques were used to analyze the nonvolatile and volatile components and composition of the tea. The results revealed significant differences (p < 0.05) in the contents of nonvolatile constituents including caffeine, polyphenols, soluble sugars, free amino acids and their components, theaflavins, thearubigins, and catechins among the five different withering methods, with varying degrees of correlation between these components. A total of 227 aroma compounds were detected, and significant differences in the contents of alcohols, aldehydes, and ketones were observed. A relative odor activity value (ROVA) analysis of the aroma compounds revealed that 19 compounds had an ROVA > 1. Among them, benzylaldehyde, trans-2-decenal, decanal, benzaldehyde, nonanal, hexanal, trans-linalool, and geraniol from the shaking withering method had significantly higher ROVA values than those from the other withering methods, which may be the reason for the prominent floral and fruity aroma of shaking withering. This study revealed the impact of different withering methods on the quality of Congou black tea, providing a scientific basis for the development of Congou black tea with different flavors and the improvement of Congou black tea processing techniques.

To facilitate the realization of automated tea picking and enhance the speed and accuracy of tea leaf grading detection, this study proposes an improved YOLOv8 network for fresh tea leaf grading recognition. This approach integrates a Hierarchical Vision Transformer using Shifted Windows to replace segments of the original YOLOv8’s network architecture, thereby alleviating the computational load of dense image processing tasks and reducing computational expenses. The incorporation of an Efficient Multi-Scale Attention Module with Cross-Spatial Learning serves to attenuate the influence of irrelevant features in complex backgrounds, which in turn, elevates the model’s detection Precision. Additionally, the substitution of the loss function with SIoU facilitates a more rapid model convergence and a more precise pinpointing of defect locations. The empirical findings indicate that the enhanced YOLOv8 algorithm has achieved a marked improvement in metrics such as Precision, Recall, F1, and mAP, with increases of 3.39%, 0.86%, 2.20%, and 2.81% respectively, when juxtaposed with the original YOLOv8 model. Moreover, in external validations, the FPS enhancements over the original YOLOv8, YOLOv5, YOLOX, Faster RCNN, and SSD deep-learning models are 6.75 Hz, 10.84 Hz, 12.79 Hz, 28.24 Hz, and 21.57 Hz, respectively, and the mAP improvements in practical detection are 2.79%, 2.92%, 3.08%, 7.07%, and 3.84% respectively. The refined model not only ensures efficient and accurate tea-grading recognition but also boasts high recognition rates and swift detection capabilities, thereby establishing a foundation for the development of tea-picking robots and tea quality grading devices.

The 6-DOF Stewart parallel elevation platform serves as the platform for mounting the tea-picking robotic arm, significantly impacting the operational scope, velocity, and harvesting precision of the robotic arm. Utilizing the Stewart setup, a parallel elevation platform with automated lifting and leveling capabilities was devised, ensuring precise halts at designated elevations for seamless harvesting operations. The effectiveness of the platform parameter configuration and the reasonableness of the posture changes were verified. Firstly, the planting mode and growth characteristics of Yunnan large-leaf tea trees were analyzed to determine the preset path, posture changes, and mechanism stroke of the Stewart parallel lifting platform, thereby determining the basic design specifications of the platform. Secondly, a 3D model was established using SolidWorks, a robust adaptive PD control model was built using MATLAB for simulation, and dynamic calculations were carried out through data interaction in Simulink and ADAMS. Finally, the rationality of the lifting platform design requirements was determined based on simulation data, a 6-DOF Stewart parallel lifting platform was manufactured, and a motion control system was built for experimental verification according to the design specifications and simulation data. The results showed that the maximum deviation angle around the X, Y, and Z axes was 10°, the maximum lifting distance was 15 cm, the maximum load capacity was 60 kg, the platform response error was within ±0.1 mm, and the stable motion characteristics reached below the millimeter level, which can meet the requirements of automated operation of the auxiliary picking robotic arm.

Bacteriophages (phages) are one of the critical biotic drivers of prokaryotic community dynamics, functions, and evolution. Despite their importance in aquatic ecosystems, very few phages have been isolated from freshwater lakes, hampering our understanding of their ecological importance and usage in a variety of biotechnological applications. Limnohabitans, with a ubiquitous distribution, is a metabolically versatile, fast-growing, morphologically diverse freshwater lake bacterial genera. It is especially abundant in pH-neutral and alkaline aquatic habitats, where it represents an average of 12% of freshwater bacterioplankton and plays an important role in funneling carbon from primary producers to higher trophic levels. However, no phages infecting Limnohabitans have been reported to date. Here, we describe, for the first time, three phages infecting Limnohabitans, DC31, DC33, and YIMV22061, isolated from two freshwater lakes in China and characterized using genome content analysis and comparative genomics. DC31 and DC33, recovered from the eutrophic Dianchi Lake, with auxiliary metabolic genes (AMGs), associated with nucleotide metabolism, whereas YIMV22061, isolated from the oligotrophic Fuxian Lake, carried AMGs involved in antibiotic resistance. The AMGs they carried highlight their impacts on Limnohabitans in different environments. Comparative genomic analyses indicate that DC31, DC33, and YIMV22061 represent three novel species in the Caudoviricetes class. IMG/VR database alignment further reveal that these phages are widely distributed across diverse aquatic and terrestrial ecosystems globally, suggesting their ecological significance. This study provides a basis for better understanding Limnohabitans–phage interactions.

Microorganisms are the core drivers of ripened Pu-erh tea (RpPT) quality formation. However, the mechanisms of the microbiological and flavor chemical changes during the fermentation process remain unclear, which limits the quality control of ripened Pu-erh tea fermentation. A physicochemical analysis revealed that fermentation significantly reduced the contents of water extracts, tea polyphenols, free amino acids, soluble sugars, and most catechins (p < 0.05), while flavonoids and tea pigments were significantly accumulated. Amplicon sequencing revealed that Pantoea and Bacillus were the dominant bacterial genera during the early and late fermentation stages, with maximum relative abundances of 97.89% and 63.66%, respectively. Aspergillus and Thermomyces were the dominant fungal genera, reaching maximum relative abundances of 98.34% and 62.59%, respectively. During fermentation, bacterial diversity initially increased and then declined, whereas fungal diversity dropped sharply and subsequently stabilized. Co-occurrence network analysis indicated that microbial interactions were primarily cooperative, with network complexity gradually diminishing as fermentation progressed. Functional prediction showed enhanced microbial metabolism of amino acids and carbohydrates during the mid-to-late stages. Crucially, the stage-specific microbial communities were significantly correlated with distinct flavor compounds, shaping the final aroma profile of ripened Pu-erh tea. These dynamics explain the variations in tea quality and provide critical insights for optimizing the fermentation process.