China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science.

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.

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 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.

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.

Understanding the correlation between soil chemical properties and tea quality is essential for the comprehensive management of ancient tea gardens. However, the specific links between these factors in ancient tea gardens remain underexplored. This study analyzes the soil chemical properties of four distinct research regions in Nanhua County to explore their effects on key chemical components in ancient tea garden teas, providing a scientific basis for improving the quality of ancient tea garden teas through soil management. Employing high performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICP-MS), the chemical components of tea and the chemical properties of the soil were meticulously quantified. Following these measurements, the integrated fertility index (IFI) and the potential ecological risk index (PERI) were evaluated and correlation analysis was conducted. The results revealed that ancient tea garden tea quality is closely linked to soil chemical properties. Soil’s total nitrogen (TN), total sulfur (TS), and available potassium (AK) negatively correlate with tea’s catechin gallate (CG) component and AK also with polyphenols. Most other soil properties show positive correlations with tea components. The research also evaluated soil heavy metals’ IFI and PERI. IFI varied significantly among regions. Hg’s high pollution index indicates ecological risks; Cd in Xiaochun (XC) region poses a moderate risk. PERI suggests moderate risk for XC and Banpo (BP), with other areas classified as low risk. Implementing reasonable fertilization and soil amelioration measures to enhance soil fertility and ensure adequate supply of key nutrients will improve the quality of ancient tea gardens. At the same time, soil management measures should effectively control heavy metal pollution to ensure the quality and safety of tea products. Insights from this study are crucial for optimizing soil management in ancient tea gardens, potentially improving tea quality and sustainability.