Although the pathway and transcription factor regulation of anthocyanin biosynthesis in tea plants [Camellia sinensis (L.) O. Ktze] are known, post-transcriptional regulation mechanisms involved in anthocyanin accumulation have not been comprehensively studied. We obtained the full-length transcriptome of a purple cultivar (9Zijuan9) and a normal green cultivar (9Yunkang 10^#9) of C. sinensis var. asssamica (Masters) showing different accumulation of anthocyanins and catechins through PacBio isoform sequencing (Iso-Seq). In total, 577,557 mapped full-length cDNAs were obtained, and 2,600 average-length gene isoforms were identified in both cultivars. After gene annotations and pathway predictions, we found that 98 key genes in anthocyanin biosynthesis pathways could have undergone alternative splicing (AS) events, and identified a total of 238 isoforms involved in anthocyanin biosynthesis. We verified expression of the C4H, CHS, FLS, CCOM, F3959H, LAR, PAL, CCR, CYP73A13, UDP75L12, UDP78A15/UFGT, UDP94P1, GL3, MYB113, ANR, ANS, F3H, 4CL1, CYP98A3/C3H, CHI, DFR genes and their AS transcripts using qRT-PCR. Correlation analysis of anthocyanin biosynthesis and gene expression results revealed that C4H1, FLS1, PAL2, CCR2, UDP75L122 and MYB113-1 are crucial AS transcripts for regulating anthocyanin biosynthesis in C. sinensis var. assamica. Our results reveal post-transcriptional regulation of anthocyanin biosynthesis in tea plants, and provide more new insights into the regulation of secondary metabolism.

Pest infestations in tea gardens are one of the common issues encountered during tea cultivation. This study introduces an improved YOLOv8 network model for the detection of tea pests to facilitate the rapid and accurate identification of early-stage micro-pests, addressing challenges such as small datasets and the difficulty of extracting phenotypic features of target pests in tea pest detection. Based on the original YOLOv8 network framework, this study adopts the SIoU optimized loss function to enhance the model’s learning ability for pest samples. AKConv is introduced to replace certain network structures, enhancing feature extraction capabilities and reducing the number of model parameters. Vision Transformer with Bi-Level Routing Attention is embedded to provide the model with a more flexible computation allocation and improve its ability to capture target position information. Experimental results show that the improved YOLOv8 network achieves a detection accuracy of 98.16% for tea pest detection, which is a 2.62% improvement over the original YOLOv8 network. Compared with the YOLOv10, YOLOv9, YOLOv7, Faster RCNN, and SSD models, the improved YOLOv8 network has increased the mAP value by 3.12%, 4.34%, 5.44%, 16.54%, and 11.29%, respectively, enabling fast and accurate identification of early-stage micro pests in tea gardens. This study proposes an improved YOLOv8 network model based on deep learning for the detection of micro-pests in tea, providing a viable research method and significant reference for addressing the identification of micro-pests in tea. It offers an effective pathway for the high-quality development of Yunnan’s ecological tea industry and ensures the healthy growth of the tea industry.

Tea polyphenol (TPs) oxidation caused by polyphenol oxidase (PPO) in manufacturing is responsible for the sensory characteristics and health function of fermented tea, therefore, this subject is rich in scientific and commercial interests. In this work, an in vitro catalysis of TPs in liquid nitrogen grinding of sun-dried green tea leaves by PPO was developed, and the changes in metabolites were analyzed by metabolomics. A total of 441 metabolites were identified in the catalyzed tea powder and control check samples, which were classified into 11 classes, including flavonoids (125 metabolites), phenolic acids (67 metabolites), and lipids (55 metabolites). The relative levels of 28 metabolites after catalysis were decreased significantly (variable importance in projection (VIP) > 1.0, p < 0.05, and fold change (FC) < 0.5)), while the relative levels of 45 metabolites, including theaflavin, theaflavin-3′-gallate, theaflavin-3-gallate, and theaflavin 3,3′-digallate were increased significantly (VIP > 1.0, p < 0.05, and FC > 2). The increase in theaflavins was associated with the polymerization of catechins catalyzed by PPO. This work provided an in vitro method for the study of the catalysis of enzymes in tea leaves.

Grading tea leaves efficiently in a natural environment is a crucial technological foundation for the automation of tea-picking robots. In this study, to solve the problems of dense distribution, limited feature-extraction ability, and false detection in the field of tea grading recognition, an improved YOLOv8n model for tea grading and counting recognition was proposed. Firstly, the SPD-Conv module was embedded into the backbone of the network model to enhance the deep feature-extraction ability of the target. Secondly, the Super-Token Vision Transformer was integrated to reduce the model’s attention to redundant information, thus improving its perception ability for tea. Subsequently, the loss function was improved to MPDIoU, which accelerated the convergence speed and optimized the performance. Finally, a classification-positioning counting function was added to achieve the purpose of classification counting. The experimental results showed that, compared to the original model, the precision, recall and average precision improved by 17.6%, 19.3%, and 18.7%, respectively. The average precision of single bud, one bud with one leaf, and one bud with two leaves were 88.5%, 89.5% and 89.1%. In this study, the improved model demonstrated strong robustness and proved suitable for tea grading and edge-picking equipment, laying a solid foundation for the mechanization of the tea industry.

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.

As a perennial plant, the nutrient supply for tea bushes is predominantly dependent on the soil. Yunnan tea plantations exhibit significant topographic slope variations, yet the combined impact of slope positions on soil chemistry and microbial communities remains unexplored. This study investigated soil chemical properties and microbial community structures across three distinct slope areas within a single tea plantation. The results showed that the contents of organic matter (OM), total nitrogen (TN), and available nutrients (AN) at the top of the slope (TS) were significantly higher than those at the foot of the slope (FS) (p < 0.001), while the cation exchange capacity (CEC) and total potassium (TK) reached peak levels in the middle of the slope (MS), with FS having the lowest nutrient levels. Redundancy analysis (RDA) indicated that bacterial communities were primarily influenced by TK, magnesium (Mg), CEC, total phosphorus (TP), and pH, whereas fungal communities were mainly regulated by TK, Mg, and CEC, highlighting the role of soil chemical properties in shaping microbial diversity and distribution. Bacterial composition showed no significant slope-related differences, but fungal communities varied notably at the family/genus levels. MS exhibited the highest microbial network complexity, suggesting stronger species interactions. Bacterial metabolic functions and fungal trophic modes were conserved across regions, indicating functional stability independent of structural changes. This study reveals slope-driven soil-microbial dynamics in Yunnan tea plantations, offering insights into microbial assembly and adaptation under topographic gradients. These findings support precision fertilization, ecological conservation, and the sustainable management of slope tea plantations.