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.

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.

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.

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.