Yunnan Province, China, known for its superior ecological environment and diverse climate conditions, is home to a rich resource of tea-plant varieties. However, the subtle differences in shape, color and size among the fresh leaves of different tea-plant varieties pose significant challenges for their identification and detection. This study proposes an improved YOLOv8 model based on a dataset of fresh leaves from five tea-plant varieties among Yunnan large-leaf tea trees. Dynamic Upsampling replaces the UpSample module in the original YOLOv8, reducing the data volume in the training process. The Efficient Pyramid Squeeze Attention Network is integrated into the backbone of the YOLOv8 network to boost the network’s capability to handle multi-scale spatial information. To improve model performance and reduce the number of redundant features within the network, a Spatial and Channel Reconstruction Convolution module is introduced. Lastly, Inner-SIoU is adopted to reduce network loss and accelerate the convergence of regression. Experimental results indicate that the improved YOLOv8 model achieves precision, recall and an mAP of 88.4%, 89.9% and 94.8%, representing improvements of 7.1%, 3.9% and 3.4% over the original model. This study’s proposed improved YOLOv8 model not only identifies fresh leaves from different tea-plant varieties but also achieves graded recognition, effectively addressing the issues of strong subjectivity in manual identification detection, the long training time of the traditional deep learning model and high hardware cost. It establishes a robust technical foundation for the intelligent and refined harvesting of tea in Yunnan’s tea gardens.

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 achieve an efficient, non-destructive, and intelligent identification of tea plant seedlings under high-temperature stress, this study proposes an improved YOLOv11 model based on chlorophyll fluorescence imaging technology for intelligent identification. Using tea plant seedlings under varying degrees of high temperature as the research objects, raw fluorescence images were acquired through a chlorophyll fluorescence image acquisition device. The fluorescence parameters obtained by Spearman correlation analysis were found to be the maximum photochemical efficiency (Fv/Fm), and the fluorescence image of this parameter is used to construct the dataset. The YOLOv11 model was improved in the following ways. First, to reduce the number of network parameters and maintain a low computational cost, the lightweight MobileNetV4 network was introduced into the YOLOv11 model as a new backbone network. Second, to achieve efficient feature upsampling, enhance the efficiency and accuracy of feature extraction, and reduce computational redundancy and memory access volume, the EUCB (Efficient Up Convolution Block), iRMB (Inverted Residual Mobile Block), and PConv (Partial Convolution) modules were introduced into the YOLOv11 model. The research results show that the improved YOLOv11-MEIP model has the best performance, with precision, recall, and mAP50 reaching 99.25%, 99.19%, and 99.46%, respectively. Compared with the YOLOv11 model, the improved YOLOv11-MEIP model achieved increases of 4.05%, 7.86%, and 3.42% in precision, recall, and mAP50, respectively. Additionally, the number of model parameters was reduced by 29.45%. This study provides a new intelligent method for the classification of high-temperature stress levels of tea seedlings, as well as state detection and identification, and provides new theoretical support and technical reference for the monitoring and prevention of tea plants and other crops in tea gardens under high temperatures.

To address the issue of drought level confusion in the detection of drought stress during the seedling stage of the Yunnan large-leaf tea variety using the traditional YOLOv13 network, this study proposes an improved version of the network, MC-YOLOv13-L, based on animal vision. With the compound eye’s parallel sampling mechanism at its core, Compound-Eye Apposition Concatenation optimization is applied in both the training and inference stages. Simulating the environmental information acquisition and integration mechanism of primates’ “multi-scale parallelism—global modulation—long-range integration,” multi-scale linear attention is used to optimize the network. Simulating the retinal wide-field lateral inhibition and cortical selective convergence mechanisms, CMUNeXt is used to optimize the network’s backbone. To further improve the localization accuracy of drought stress detection and accelerate model convergence, a dynamic attention process simulating peripheral search, saccadic focus, and central fovea refinement in primates is used. Inner-IoU is applied for targeted improvement of the loss function. The testing results from the drought stress dataset (324 original images, 4212 images after data augmentation) indicate that, in the training set, the Box Loss, Cls Loss, and DFL Loss of the MC-YOLOv13-L network decreased by 5.08%, 3.13%, and 4.85%, respectively, compared to the YOLOv13 network. In the validation set, these losses decreased by 2.82%, 7.32%, and 3.51%, respectively. On the whole, the improved MC-YOLOv13-L improves the accuracy, recall rate and mAP@50 by 4.64%, 6.93% and 4.2%, respectively, on the basis of only sacrificing 0.63 FPS. External validation results from the Laobanzhang base in Xishuangbanna, Yunnan Province, indicate that the MC-YOLOv13-L network can quickly and accurately capture the drought stress response of tea plants under mild drought conditions. This lays a solid foundation for the intelligence-driven development of the tea production sector and, to some extent, promotes the application of bio-inspired computing in complex ecosystems.

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.

This study focuses on the renewal and iteration mechanisms of aged tea trees in interactions with their soil microbial communities, aiming to elucidate the impact of the planting age of tea trees on the structure and function of soil microbial communities and how these impacts are linked to the formation of tea quality. By conducting a comparative analysis of the cultivation soil from tea trees with varying planting ages ranging from 30 to 200 years, we employed microbial diversity sequencing, a soil physicochemical property analysis, and tea leaf chemical component detection. We combined these methods with redundancy analysis (RDA) and linear discriminant analysis effect size (LEfSe) to reveal significant correlations between the planting age of tea trees and the soil’s microbial diversity and function. The results indicate that as the planting age of tea trees increases, there are significant changes in the soil’s pH and nutrient content. Concurrently, the components of the tea leaves also change. Most notably, around the 120 years mark of the tea tree planting age, the diversity of the soil microbial community reaches a turning point. Key microbial community analyses revealed shifts in the dominant microbial populations within the soil across the various tea tree planting ages, exemplified by taxa such as Hygrocybe Mycena, Humicola, Bradyrhizobium, and Candidatus Solibacter. These alterations in microbial communities are closely associated with soil nutrient dynamics and the developmental stages of tea trees. These findings not only provide scientific guidance for tea garden management, tea tree cultivation, and tea production but also offer new insights into the impact of tea tree–soil–microbe interactions on tea quality, which is significantly important for enhancing tea quality.