Search Results (125)

Tea’s chemical composition is influenced by cultivar, harvest maturity, and growing environment; however, processing remains the dominant factor shaping final quality. Despite the diversity of Taiwanese native teas, systematic comparisons of functional components across multiple manufacturing stages remain limited. In this study, nine representative Taiwanese teas were evaluated at four key processing stages—green tea (G), enzymatic fermentation (oxidative fermentation, F), semi-finished tea prior to roasting (S), and completed tea (C)—to clarify how enzymatic oxidation, rolling, and roasting alter major bioactive constituents. Green-tea-stage samples exhibited clear cultivar-dependent profiles: large-leaf cultivars contained higher catechins and gallic acid, whereas bud-rich small-leaf teas showed elevated caffeine and amino acids, with amino acids further enhanced at higher elevations. Fermentation intensity governed the major chemical transitions, including catechin depletion, gallic acid formation, accumulation of early stage catechin-derived paired oxidative polymerization compounds (POPCs), and pronounced increases in theasinensins in heavily fermented teas. L-theanine decreased most markedly in teas subjected to prolonged withering. Roasting further reduced amino acids but had minimal influence on caffeine, while rolling effects varied by tea type. Overall, this study provides the first stage-resolved chemical map of Taiwanese native teas, offering practical insights for optimizing processing strategies to enhance functional phytochemical profiles. Full article

Agroforestry intercropping is increasingly recognized for improving soil quality and crop productivity, yet its effects on soil nutrient dynamics, enzyme activities across soil profiles, and tea yield remain insufficiently understood. Here, we assessed how four systems—monoculture tea (CK), Osmanthus–tea (OT), Michelia–tea (MT), and Osmanthus–Michelia–tea (OMT)—influence soil properties and spring tea yield in hilly plantations of southern China. Across systems, the OMT configuration produced the highest spring tea yield, representing a 39.5% increase relative to CK, accompanied by a 19.0% increase in tea bud density. In the 0–20 cm soil layer, OMT markedly enhanced soil organic matter by 48.4%, total nitrogen by 25.8%, and available nitrogen and phosphorus by 24.9% and significant margins, respectively, while also stimulating enzyme activities—urease (+34.1%), sucrase (+17.2%), dehydrogenase (+43.9%), amylase (+17.2%), and cellulase (+60.7%). In the 20–40 cm layer, OMT increased soil organic matter (+48.4%), total nitrogen (+25.8%), and available nitrogen, and elevated key enzyme activities, including sucrase (+46.5%), acid phosphatase (+16.3%), and polyphenol oxidase (+20.1%). Correlation and principal component analyses further revealed strong positive associations among nutrient enrichment, enzyme activation, and tea yield. These findings demonstrate that the OMT agroforestry configuration enhances nutrient availability and enzymatic function throughout the soil profile, thereby promoting higher tea yield. Overall, OMT substantially improved spring-season soil fertility and productivity, highlighting its potential for sustainable tea plantation management. Full article

Background: Heterosis (hybrid vigor) is a fundamental phenomenon in plant breeding, but its molecular basis remains poorly understood in perennial crops such as tea (Camellia sinensis). This study aimed to elucidate the molecular mechanisms underlying heterosis in tea and its hybrids by performing integrated transcriptomic and metabolomic analyses of F₁ hybrids derived from two elite cultivars, Fuding Dabaicha (FD) and Baojing Huangjincha 1 (HJC). Methods: Comprehensive RNA sequencing and widely targeted metabolomic profiling were conducted on the parental lines and F₁ hybrids at the one-bud-one-leaf stage. Primary metabolites (including amino acids, nucleotides, saccharides, and fatty acids) were quantified, and gene expression profiles were obtained. Transcriptomic and metabolomic datasets were integrated using KEGG pathway enrichment and co-expression network analysis to identify coordinated molecular changes underlying heterosis. Results: Metabolomic profiling detected 977 primary metabolites, many of which displayed non-additive accumulation patterns. Notably, linoleic acid derivatives (9(S)-HODE, 13(S)-HODE) and nucleotides (guanosine, uridine) exhibited significant positive mid-parent heterosis. Transcriptomic analysis revealed extensive non-additive gene expression in F₁ hybrids, and upregulated genes were enriched in fatty acid metabolism, nucleotide biosynthesis, and stress signaling pathways. Integrated analysis demonstrated strong coordination between differential gene expression and metabolite accumulation, especially in linoleic acid metabolism, cutin/suberine biosynthesis, and pyrimidine metabolism. Positive correlations between elevated fatty acid levels and transcript abundance of lipid metabolism genes suggest that the transcriptional remodeling of lipid pathways contributes to heterosis. Conclusions: These findings provide novel insights into tea plant heterosis and identify potential molecular targets for breeding high-quality cultivars. Full article

Root system analysis remains methodologically challenging in plant research: traditional soil cultivation obstructs comprehensive root observation, whereas hydroponic visualization lacks ecological relevance due to soil environment exclusion—a critical limitation for crops like soybean. This manuscript developed a cost-effective hybrid imaging system integrating transparent acrylic plates, semi-permeable membranes, and natural soil substrates with high-resolution imaging and controlled illumination, enabling non-destructive root monitoring in quasi-natural soil conditions. Complementing this hardware innovation, this manuscript proposed an unsupervised semantic segmentation algorithm that synergizes path planning with an enhanced DBSCAN framework, achieving the precise extraction of primary and lateral root architectures. Experimental validation demonstrated superior performance in soybean root analysis, with segmentation metrics reaching 0.8444 accuracy, 0.9203 recall, 0.8743 F1-score, and 0.7921 mIoU—significantly outperforming existing unsupervised methods ($p<0.01$). Strong correlations (R² > 0.94) with WinRHIZO in quantifying root length, projected area, dimensional parameters, and lateral root counts confirmed system reliability. This soil-compatible phenotyping platform establishes new opportunities for root research, with future developments targeting multi-crop adaptability and complex soil condition applications through modular hardware redesign and 3D reconstruction algorithm integration. Full article

Focusing on the mechanical characteristics of robotic dexterous hand tea-picking, this paper takes the harvesting of the premium tea Huangshan Maofeng as an example and proposes an adaptive impedance control method for tea-picking dexterous hands based on the Whale Optimization Algorithm (WOA) and Kolmogorov–Arnold Network (KAN). Firstly, within the impedance control framework, a KAN neural network with cubic B-spline functions as activation functions is introduced. Subsequently, the WOA is applied to optimize the B-splines, enhancing the network´s nonlinear fitting and global optimization capabilities, thereby achieving dynamic mapping and real-time adjustment of impedance parameters to improve the accuracy of tea bud contact force-tracking. Finally, simulation results show that under working conditions such as stiffness mutation and dynamic changes in desired force, the proposed method reduces the overshoot by 14.2% compared to traditional fixed-parameter impedance control, while the steady-state error is reduced by 99.89%. Experiments on tea-picking using a dexterous hand equipped with tactile sensors show that at a 50Hz control frequency, the maximum overshoot is about 6%, further verifying the effectiveness of the proposed control algorithm. Full article

Due to the shortage of rural labor and an increasingly aging population, promoting the mechanized plucking of bulk tea and improving plucking efficiency have become urgent problems for tea plantations. Previous bulk tea plucking machines have not fully adapted to tea plantations in hilly areas, necessitating enhancements in the performance of cutter profiling. In this paper, we present an automatic cutter profiling method based on an RGB-D camera, which utilizes the depth information of bulk tea shoots to tackle the issues mentioned above. Specifically, we use improved super-green features and the Otsu method to detect and segment the shoots from the RGB images of the tea canopy taken from different lighting conditions. Furthermore, the cutting pose based on the depth value of the tea shoots can be generated as a basis for cutter profiling. Lastly, the profiling task is completed by the upper computer controlling motors to adjust the cutter pose. Field tests were conducted in the tea plantation to verify the proposed profiling method’s effectiveness. The average bud and leaf integrity rate, leakage rate, loss rate, tea making rate, and qualified rate were 81.2%, 0.91%, 0.66%, and 90.4%, respectively. The results show that the developed algorithm can improve cutting pose calculation accuracy and that the harvested bulk tea shoots meet the requirements of machine plucking quality standards and the subsequent processing process. Full article

Tea red scab, a contagious disease affecting tea plants, can infect both buds and mature leaves. This study developed discrimination models to assess the severity of this disease using RGB and hyperspectral images. The models were constructed from a total of 1188 samples collected in May 2024. The results demonstrated that the model based on hyperspectral Imaging (HSI) data significantly outperformed the RGB-based model. Four spectral preprocessing methods were applied, among which the combination of SNV, SG, and FD (SNV-SG-FD) proved to be the most effective. To better capture long-range dependencies among spectral bands, a hybrid architecture integrating a Gated Recurrent Unit (GRU) with a one-dimensional convolutional neural network (1D-CNN), termed CNN-GRU, was proposed. This hybrid model was compared against standalone CNN and GRU benchmarks. The hyperparameters of the CNN-GRU model were optimized using the Newton-Raphson-based optimizer (NRBO) algorithm. The proposed NRBO-optimized SNV-SG-FD-CNN-GRU model achieved superior performance, with accuracy, precision, recall, and F₁-score reaching 92.94%, 92.54%, 92.42%, and 92.43%, respectively. Significant improvements were observed across all evaluation metrics compared to the single-model alternatives, confirming the effectiveness of both the hybrid architecture and the optimization strategy. Full article

Aiming at the low qualification rate and high damage caused by the lack of identification, localization, and posture estimation of tea buds in the mechanical harvesting process of famous tea, a framework of lightweight detection + PCA-skeleton fusion posture estimation was proposed. Based on the YOLOv8n model, the StarNet backbone network was introduced to enable lightweight detection, and the ASF-YOLO multi-scale attention module was embedded to improve the feature fusion ability. Based on the detection frame, the GrabCut-Watershed fusion segmentation was employed to obtain the bud mask. Combined with PCA and skeleton extraction algorithms, the main direction deviations of bent buds and clasped leaves were solved by Bézier curve fitting, and the morphology–posture dual-factor scoring model was thereby constructed to realize the picking ranking. Compared with the original YOLOv8n model, the results showed that the detection accuracy and mAP50 of the Improved model decreased to 85.6% and 90.5%, respectively, and the recall rate increased to 81.7%. Meanwhile, the calculation load of the improved model was reduced by 23.6%, reaching 6.8 GFLOPs, indicating a significant improvement in lightweight. The morphology–posture dual-factor scoring model achieved a score of 0.88 for a single bud in vertical direction (θ ≈ 90°), a score of approximately 0.66–0.71 for buds with partially unfolded leaves and slightly bent buds, and a score of 0.48–0.53 for severely bent and overlapped buds. The results of this study have the potential to guide the picking robotic arms to preferentially pick tea buds with high adaptability and provide a reliable visual solution for low-loss and high-efficiency mechanized harvesting of famous tea in complex tea gardens. Full article

Polyploids play significant roles in tea production due to their strong tolerance to adverse environmental conditions and their high levels of certain chemical components. Tetraploid can be used to produce more polyploid tea plants, but there have been only a handful of tetraploids found in tea plants. In spite of the extremely low probabilities, bud mutant selection is an effective way to obtain polyploid tree crops. In the present study, a Dashu tea, cytochimera, derived from a bud mutation was identified by using flow cytometry and chromosome observation. The morphology and photosynthetic characteristics of leaves were investigated briefly. Some chemical components were determined. Finally, the pollen viability and ploidy of progeny were detected. The results show that tetraploid cells account for 71.48 ± 3.88%–72.19 ± 2.80% of the leaf tissue in this cytochimera. Compared with the original diploid, the cytochimera exhibited broader, longer, and thicker leaves. Its net photosynthetic rate (high to 41.77 ± 0.38 μmol CO₂·m⁻²·s⁻¹) was higher than that of the original diploid (peak value 28.00 ± 2.29 μmol CO₂·m⁻²·s⁻¹) for most of the day when measured in September. Notably, the total content of 19 free amino acids in the tender spring shoots of cytochimera was 22.96 ± 0.58 mg/g, approximately twice of that of the diploid materials analyzed. The contents of 10 free amino acids, including theanine, were significantly higher than those in diploids, with some free amino acid contents reaching up to seven times those observed in diploids. In addition, the cytochimera produced larger pollen grains than the original diploid, although the in vitro germination rate was lower (14.63 ± 1.11%). Three open-pollinated progenies of cytochimera were identified as triploids. To sum up, cytochimera has larger and thicker leaves, a higher photosynthetic rate, and higher content of total free amino acids and some free amino acids, especially theanine, than the original diploid. Moreover, cytochimera has a certain level of fertility and can produce triploids. These findings suggest the potential for selecting polyploid tea plants from bud mutants and for developing new tea germplasms with enhanced amino acid contents. Full article

Tea bud targets are typically located in complex environments characterized by multi-scale variations, high density, and strong color resemblance to the background, which pose significant challenges for rapid and accurate detection. To address these issues, this study presents YOLO-LMTB, a lightweight multi-scale detection model based on the YOLOv11n architecture. First, a Multi-scale Edge-Refinement Context Aggregator (MERCA) module is proposed to replace the original C3k2 block in the backbone. MERCA captures multi-scale contextual features through hierarchical receptive field collaboration and refines edge details, thereby significantly improving the perception of fine structures in tea buds. Furthermore, a Dynamic Hyperbolic Token Statistics Transformer (DHTST) module is developed to replace the original PSA block. This module dynamically adjusts feature responses and statistical measures through attention weighting using learnable threshold parameters, effectively enhancing discriminative features while suppressing background interference. Additionally, a Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the original network structure, enabling the adaptive fusion of semantically rich and spatially precise features via bidirectional cross-scale connections while reducing computational complexity. In the self-built tea bud dataset, experimental results demonstrate that compared to the original model, the YO-LO-LMTB model achieves a 2.9% improvement in precision (P), along with increases of 1.6% and 2.0% in mAP50 and mAP50-95, respectively. Simultaneously, the number of parameters decreased by 28.3%, and the model size reduced by 22.6%. To further validate the effectiveness of the improvement scheme, experiments were also conducted using public datasets. The results demonstrate that each enhancement module can boost the model’s detection performance and exhibits strong generalization capabilities. The model not only excels in multi-scale tea bud detection but also offers a valuable reference for reducing computational complexity, thereby providing a technical foundation for the practical application of intelligent tea-picking systems. Full article

Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to factors such as the small size, high density, severe overlap, and the similarity in color between tea shoots and the background. Consequently, this paper proposes an improved target detection algorithm, RFA-YOLOv8, based on YOLOv8, which aims to enhance the detection accuracy and robustness of tea shoots in natural environments. First, a self-constructed dataset containing images of tea shoots under various lighting conditions is created for model training and evaluation. Second, the multi-scale feature extraction capability of the model is enhanced by introducing RFCAConv along with the optimized SPPFCSPC module, while the spatial perception ability is improved by integrating the RFAConv module. Finally, the EIoU loss function is employed instead of CIoU to optimize the accuracy of the bounding box positioning. The experimental results demonstrate that the improved model achieves 84.1% and 58.7% in mAP@0.5 and mAP@0.5:0.95, respectively, which represent increases of 3.6% and 5.5% over the original YOLOv8. Robustness is evaluated under strong, moderate, and dim lighting conditions, yielding improvements of 6.3% and 7.1%. In dim lighting, mAP@0.5 and mAP@0.5:0.95 improve by 6.3% and 7.1%, respectively. The findings of this research provide an effective solution for the high-precision detection of tea shoots in complex lighting environments and offer theoretical and technical support for the development of smart tea gardens and automated picking. Full article

Lycium barbarum L. bud tea and leaf tea are functional processed products made from L. barbarum buds and leaves with traditional green tea processing techniques. Based on an extensive targeted metabolomics technology, this study systematically analyzed the chemical composition of L. barbarum bud tea and leaf tea, identified their differential compounds, and explored the effects of water-extracted substances on the activities of pancreatic lipase and α-Amylase. The results showed that the contents of total phenols, total flavonoids, and chlorogenic acid in the bud tea were 36.09 ± 1.97 mg/g, 7.44 ± 0.31 mg/g, and 4.18 ± 0.10 mg/g, respectively, 66.25%, 34.78%, and 22.58% higher than those in the leaf tea, respectively. A total of 594 metabolites were identified through the metabolomics analysis, mainly including flavonoids, phenolic acid compounds, alkaloids, amino acids and their derivatives, organic acids, lignans and coumarins, terpenoids, ands steroid compounds. Among them, flavonoids, phenolic acids, alkaloids, and amino acids and their derivatives accounted for approximately 58%. Compared with the leaf tea, the bud tea was significantly enriched with flavonoids, phenolic acid compounds, nucleotide compounds, lignans, and coumarins. Delphinidin 3-O-galactoside, cyanidin-3-glucoside, and cyanidin-3-O-glucoside were identified as significantly differential metabolites. Both L. barbarum bud tea and leaf tea exhibited good inhibitory effects on pancreatic lipase and α-Amylase, with the highest inhibition rates being 68.71%, 77.33%, 76.08%, and 69.96%, respectively. The contents of anthocyanins and their derivatives, including delphinidin-3-O-galactoside, cyanidin-3-glucoside, cyanidin-3-O-glucoside, cyanidin-O-hexoside, delphinidin-O-hexoside, and delphinidin diglucoside, were positively correlated with the activities of the two enzymes. These results underpin functional exploration and quality standardization of L. barbarum bud/leaf tea products. Full article

To investigate the effects of intercropping green manure on the tea plantation ecosystem, this study was conducted using 40-year-old Camellia sinensis cv. “Fuding Dabai” tea plants at the Tea Experimental Base of the Jiangxi Institute of Cash Crops. Four treatments were established: clean tillage (CK), tea intercropped with ryegrass (Lolium perenne, TRG), tea intercropped with rapeseed (Brassica napus, TRP), and tea intercropped with alfalfa (Medicago sativa, TAL). The study systematically evaluated the effects of green manure on tea yield, soil nutrient content, enzyme activity, and microbial community structure. The results showed that intercropping with green manure significantly increased the bud density, hundred-bud weight, and yield of tea in spring, summer, and autumn, with the TAL treatment showing the best overall performance. In terms of soil physicochemical properties, green manure treatments significantly improved soil organic matter, total nitrogen, available nitrogen, available phosphorus, and available potassium contents, with TRP and TAL showing the most pronounced improvements. Enzyme activity analysis indicated that the TRP treatment significantly enhanced the activities of amylase, urease, and invertase. High-throughput sequencing results revealed that green manure treatments significantly increased both the number of bacterial and fungal OTUs (Operational Taxonomic Units) and alpha diversity indices. The TAL and TRP treatments showed superior performance in terms of Shannon, Chao, and ACE indices compared to CK. Principal coordinate analysis (PCoA) indicated that green manure had a greater influence on fungal community structure than on bacterial structure. Correlation analysis demonstrated that dominant microbial taxa were significantly associated with soil nitrogen, phosphorus, and potassium levels, suggesting that green manure modulates microbial community composition by improving soil nutrient status. Intercropping green manure significantly increased tea yield and soil quality compared with clean tillage. Alfalfa intercropping (TAL) increased tea yield by 49.61%, 40.88%, and 43.79% in spring, summer, and autumn, respectively, compared with the control. Soil organic matter and total nitrogen under TAL were 29.02% and 15.67% higher than the control, while rapeseed intercropping (TRP) increased available phosphorus by 186%. TAL and TRP also enhanced microbial diversity, with bacterial Shannon index values 14.11% and 11.25% higher than the control. These results indicate that alfalfa intercropping is the most effective green manure practice for improving tea plantation productivity and soil ecology. Full article

The accurate detection of tea buds in outdoor environments is crucial for the intelligent management of modern tea plantations. However, this task remains challenging due to the small size of tea buds and the limited computational capabilities of the edge devices commonly used in the field. Existing object detection models are typically burdened by high computational costs and parameter loads while often delivering suboptimal accuracy, thus limiting their practical deployment. To address these challenges, we propose TeaBudNet, a lightweight and robust detection framework tailored for small tea bud identification under outdoor conditions. Central to our approach is the introduction of Weight-FPN, an enhanced variant of the BiFPN designed to preserve fine-grained spatial information, thereby improving detection sensitivity to small targets. Additionally, we incorporate a novel P2 detection layer that integrates high-resolution shallow features, enhancing the network’s ability to capture detailed contour information critical for precise localization. To further optimize efficiency, we present a Group–Taylor pruning strategy, which leverages Taylor expansion to perform structured, non-global pruning. This strategy ensures a consistent layerwise evaluation while significantly reducing computational overhead. Extensive experiments on a self-built multi-category tea dataset demonstrate that TeaBudNet surpasses state-of-the-art models, achieving +5.0% gains in AP@50 while reducing parameters and computational cost by 50% and 3%, respectively. The framework has been successfully deployed on Huawei Atlas 200I DKA2 developer kits in real-world tea plantation settings, underscoring its practical value and scalability for accurate outdoor tea bud detection. Full article

Understanding how leaf maturity affects the flavor attributes of green tea is crucial for optimizing harvest timing and processing strategies. This study comprehensively characterized the flavor profiles of Fudingdabai green teas at three distinct leaf maturity stages—single bud (FDQSG), one bud + one leaf (FDMJ1G), and one bud + two leaves (FDTC2G)—using a multimodal approach integrating electronic nose, electronic tongue, HS-GC-IMS, relative odor activity value (rOAV) evaluation, and machine learning algorithms. A total of 85 volatile compounds (VOCs) were identified, of which 41 had rOAV > 1. Notably, 2-methylbutanal, 2-ethyl-3,5-dimethylpyrazine, and linalool exhibited extremely high rOAVs (>1000). FDQSG was enriched with LOX (lipoxygenase)-derived fresh, grassy volatiles such as (Z)-3-hexen-1-ol and nonanal. FDMJ1G showed a pronounced accumulation of floral and fruity compounds, especially linalool (rOAV: 7400), while FDTC2G featured Maillard- and phenylalanine-derived volatiles like benzene acetaldehyde and 2,5-dimethylfuran, contributing to roasted and cocoa-like aromas. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed significant enrichment in butanoate metabolism and monoterpenoid biosynthesis. Random forest–SHAP analysis identified 20 key flavor markers, mostly VOCs, that effectively discriminated samples by tenderness grade. ROC–AUC validation further confirmed their diagnostic performance (accuracy ≥ 0.8). These findings provide a scientific basis for flavor-driven harvest management and the quality-oriented grading of Fudingdaibai green tea. Full article