To investigate the flavour evolution mechanism of raw Pu-erh tea (RPT) during storage, the volatile and non-volatile compounds of RPT with different storage years (1–10 years) from the same raw material origin, manufacturer, and storage location in Wenshan Prefecture, Yunnan Province, were systematically analysed by HPLC, HS-SPME-GC-MS, and OAV. The results showed that both cluster analyses based on non-volatile and volatile compounds could classify RPT of different storage years into three ageing cycles, with key turning points in the third and eighth years of storage, which is also accompanied by the colour changing from green to orange or brown, the aroma changing from a faint scent to woody and ageing, the astringency diminishing, and the sweet and mellow increasing. Theophylline was identified as the potential marker of RPT stored 1–3 years, while (−)-catechin gallate, (−)-gallocatechin gallate, quercetin, and rutin as those for a storage of 9–10 years. The volatile compounds indicate a general trend of an initial increase followed by a decrease. Forty-four key aroma compounds (OAV ≥ 1) were identified. Eucalyptol, β-Caryophyllene, 2-Amylfuran, Copaene, Estragole, and α-Terpinene originated as potential markers for RPT stored 1–3 years, while (Z)-Linalool oxide (furanoid), α-Terpineol, Terpinen-4-ol, and cis-Anethol were for RPT stored 8–10 years. This study revealed the flavour characteristics and quality changes of RPT over the course of storage, and constructed a sensory flavour wheel, providing theoretical underpinnings for the quality control and assessment of RPT.

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.

To elucidate the regional flavor characteristics of sun-dried green tea (SDT) and their underlying influencing factors, a comprehensive analysis was conducted using metabolomics and flavoromics approaches. This study systematically examined SDT samples and their corresponding tea garden soils from 13 distinct regions in Yunnan Province. The results revealed that the SDT samples could be classified into two distinct groups based on their flavor profiles. Compared to the regions of Pa Sha (PS), Bang Dong (BD), Dong Ban Shan (DBS), Dong Guo (DG), Su Hu (SH), Gua Feng Zhai (GFZ), and Wu Liang Shan (WLS), the regions of Xin Nong (XN), Ba Ka Nuan (BKN), Mang Ang (MA), Man Nuan (MN), Bing Dao (BDao), and Bin Shan (BS) exhibited a significant upregulation of the tea polyphenols (TP)/free amino acids (FAA) ratio. The former group was characterized by a sweet mellow taste, while the latter displayed a stronger taste profile. Furthermore, the analysis of volatile compounds demonstrated that geraniol and linalool were significantly upregulated in the PS, BD, DBS, DG, BS, and BDao regions, which were associated with tender and floral aromas. In contrast, isophorone, 2-pentyl furan, 1-octanol, D-limonene, and benzaldehyde were markedly enriched in the XN, BKN, MA, MN, SH, GFZ, and WLS regions, contributing to sweet and honey-like aromatic profiles. Altitude and mineral element phosphorus are potential key factors affecting the regional flavor differences in SDT. Specifically, SDT cultivated at higher altitudes and in soils with elevated available phosphorus content exhibited a greater likelihood of accumulating sweet mellow and floral compounds. This study provides scientific evidence for understanding the characteristic flavor profiles of SDT across different regions, offering valuable insights into the factors contributing to regional flavor differentiation in tea production.

To address the problem of detecting foreign bodies in Pu-erh tea, this study proposes an intelligent detection method based on an improved YOLOv10 network. By introducing the MPDIoU loss function, the YOLOv10 network is optimized to effectively enhance the positioning accuracy of the model in complex background and improve detection of small target foreign objects. Using AssemFormer to optimize the structure, the network’s ability to perceive small target foreign objects and its ability to process global information are improved. By introducing the Rectangular Self-Calibrated Module, the prediction accuracy of the bounding box is effectively optimized, further improving the classification and target-positioning abilities of the model in complex scenes. The results showed that the Box, Cls, and Dfl loss functions of the CV-YOLOv10-AR-M network in the One-to-Many Head task were, respectively, 14.60%, 19.74%, and 20.15% lower than those of the YOLOv10 network. In the One-to-One Head task, they decreased by 10.42%, 29.11%, and 20.15%, respectively. Compared with the original YOLOv10 network, the accuracy, recall rate, and mAP of the CV-YOLOv10-AR-M network were increased by 5.35%, 11.72% and 8.32%, respectively. The CV-YOLOv10-AR-M network effectively improves the model’s attention to small sizes, complex backgrounds, and detailed information, providing effective technical support for intelligent quality control in the agricultural field.

The terrain of tea gardens in Yunnan is complex, and the branches and leaves of tea trees are densely interlaced. Traditional manual picking methods are labor-intensive and inefficient, and can no longer meet the needs of modern tea industry development. To achieve automatic recognition of tea buds and leaves and accurate two-dimensional localization of picking points in complex natural environments, this study constructs a lightweight and high-precision tea bud and leaf detection and keypoint localization model, YOLOv11-pose-BEH, based on the YOLOv11-pose model. Based on the original network, this model introduces the BiFPN feature fusion module to achieve efficient bidirectional transmission of multi-scale information. The EMA (Efficient Multi-scale Attention) mechanism is integrated to form the C2PSA_EMA module, improving the effect of multi-scale feature extraction. HetConv is introduced to form the C3K2_HetConv module, enhancing the extraction of local texture and edge features. Compared with the baseline network, the improved YOLOv11-pose-BEH achieved significant improvements in both tea bud object detection and picking point localization. For the tea bud object detection task, the precision, recall, mAP0.5, and F1-score increased by 7.41, 6.39, 5.28, and 6.88 percentage points, respectively. For the picking point localization task, the precision, recall, mAP0.5, and F1-score increased by 5.86, 5.83, 4.83, and 5.85 percentage points, respectively. These results indicate that the proposed model can achieve more accurate and stable tea bud and leaf detection and two-dimensional picking point localization under complex backgrounds, providing efficient and reliable technical support for the visual perception of intelligent tea-picking robots in tea gardens.

Long-term aging transforms the sensory quality of raw Pu-erh tea, but the temporal coordination between taste-related and aroma-active compounds remains unclear. Here, raw Pu-erh teas aged for 1–22 years, produced in the same region and stored under consistent conditions, were characterized using sensory evaluation, targeted analysis of non-volatile constituents, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry, relative odor activity values (ROAVs), and multivariate statistics. Aging shifted the sensory profile from fresh, strong, and astringent to mellow and aged, accompanied by a change in liquor color from yellow-green to orange-red. Non-volatile profiles resolved four aging stages (1–4, 6–10, 14–18, and 19–22 years), whereas key aroma-active compounds resolved three stages (1–10, 14–19, and 20–22 years). Tea polyphenols, catechins, and free amino acids generally declined, while theabrownins, thearubigins, gallic acid, and selected flavonoids accumulated. Among 523 detected volatile compounds, 39 exhibited ROAVs ≥ 1; β-ionone and (E)-2-nonenal formed a persistent floral–woody aroma backbone, while stage-specific changes increasingly favored resinous, camphoraceous, roasted, and aged notes. Notably, differences in non-volatile composition weakened after 14 years, whereas aroma profiles continued to reorganize. This asynchronous evolution constitutes the central finding of this study, demonstrating that taste-related composition approaches relative stability before aroma maturation is complete and providing a chemical basis for aging-stage classification and quality evaluation.