Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot’s performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well.

In order to investigate the feasibility of using rice critical nitrogen concentration as a nitrogen nutrition diagnosis index, a two-year positioning field gradient experiment using four rice varieties and four nitrogen levels (0, 75, 150, 225 kg·ha^–1 for early rice; 0, 90, 180, 270 kg·ha^–1 for late rice) was conducted for early and late rice. The critical dilution curves (N_c%) of the double-cropped rice based on leaf dry matter (LDM) were constructed and verified using the field data. Two critical nitrogen dilution curves and nitrogen nutrition indexes (NNI) of rice LDM were constructed for early rice [N_c% = 2.66LDM^−0.79, R² = 0.88, NNI ranged between 0.29–1.74, and the average normalized root mean square error (n-RMSE = 19.35%)] and late rice [N_c% = 7.46LDM^−1.42, R² = 0.91, NNI was between 0.55–1.53, and the average (n-RMSE = 15.14%)]. The relationship between NNI and relative yield was a quadratic polynomial equation and suggested that the optimum nitrogen application rate for early rice was sightly smaller than 150 kg·ha^–1, and that for late rice was about 180 kg·ha^-1. The developed critical nitrogen concentration dilution curves, based on leaf dry matter, were able to diagnose nitrogen nutrition in the double-cropped rice region.

As the raw material for tea making, the quality of fresh tea leaves directly affects the quality of finished tea. Traditional manual sorting and machine sorting struggle to meet the requirements for high-quality tea processing. Based on machine vision and deep learning, intelligent grading technology has been applied to the automated sorting of fresh tea leaves. However, when faced with machine-picked tea leaves, the characteristics of complex morphology, small target recognition size, and dense spatial distribution can interfere with accurate category recognition, which in turn limits classification accuracy and consistency. Therefore, we propose an enhanced YOLOv12 detection framework that integrates three key modules—C3k2_EMA, A2C2f_DYT, and RFAConv—to strengthen the model’s ability to capture delicate tea bud features, thereby improving detection accuracy and robustness. Experimental results demonstrate that the proposed method achieves precision, recall, and mAP@0.5 of 81.2%, 90.6%, and 92.7% in premium tea recognition, effectively supporting intelligent and efficient tea harvesting and sorting operations. This study addresses the challenges of subtle fine-grained differences, small object sizes, variable morphology, and complex background interference in premium tea bud images. The proposed model not only achieves high accuracy and robustness in fine-grained tea bud detection but also provides technical feasibility for intelligent fresh tea leaves classification and production monitoring.

Tea garden irrigation suffers from time delays, nonlinear interference, and phenological biomass fluctuations caused by plucking, leading to the failure of traditional Proportional–Integral–Derivative (PID) and fixed-threshold models in precise water supply. This study proposes a precision irrigation system for smart tea gardens integrating Phenology-Aware Collaborative Decision-Making and an Adaptive Gain Predictive Super-Twisting Sliding Mode Control (AG-PSTC) algorithm. A “temperature–time–water” phenological reference model was constructed, and Crop Water Stress Index (CWSI) was introduced to decouple shoot density changes into phenology-driven and water stress components, realizing dynamic target soil moisture (W_target) setting. The AG-PSTC algorithm combined an improved Smith predictor for phase compensation and a barrier function-based adaptive super-twisting term for chattering elimination and finite-time convergence. Simulations showed AG-PSTC reduced rise time by 78% and steady-state error by four orders of magnitude compared with PID, with robust performance under ±40% time-delay perturbation. Field tests confirmed the system suppressed false irrigation during plucking, with soil moisture standard deviation within 1.51%. This study provides a vertical integration framework from crop physiological models to precision control, promoting the transition of tea garden irrigation from experience-based to demand-based.

Tea quality is largely determined by the accumulation of specialized metabolites in fresh leaves, yet the effects of the propagation method on tea quality and its belowground ecological basis remain insufficiently understood. In this study, sexually propagated (SR) and asexually propagated (AR) tea plants were compared by integrating soil physicochemical analysis, leaf quality and physiological measurements, widely targeted metabolomics, and rhizosphere metagenomic profiling. Compared with AR, SR plants exhibited more favorable rhizosphere nutrient conditions, with soil organic matter, total nitrogen, alkali-hydrolyzable nitrogen, and available phosphorus increasing by 23.1%, 18.2%, 27.8%, and 161.5%, respectively, although available potassium decreased by 24.4%. SR leaves also contained higher dry matter, tea polyphenol, and soluble sugar contents, which increased by 11.5%, 58.8%, and 8.6%, respectively. In addition, superoxide dismutase, peroxidase, and indole-3-acetic acid oxidase activities were 30.4%, 92.0%, and 21.7% higher under SR, whereas hydrogen peroxide content remained unchanged. Metabolomic profiling revealed marked differences in leaf metabolic composition between the two propagation types, with differentially accumulated metabolites mainly enriched in flavonoid biosynthesis, phenolic acid metabolism, caffeine metabolism, carotenoid biosynthesis, plant hormone signaling, and α-linolenic acid metabolism. Rhizosphere metagenomic analysis further showed that SR was characterized by higher relative abundances of Actinomycetota, Pseudomonadota, Planctomycetota, Alphaproteobacteria, and Streptomycetales, together with distinct microbial functional profiles related to glycolysis, the tricarboxylic acid cycle, and pyruvate metabolism. Significant correlations were identified between several SR-enriched microbial taxa and quality-related metabolites, particularly flavonoids and phenolic acids. Overall, under the present field conditions, sexual propagation was more favorable than asexual propagation for tea quality formation, as reflected by improved nitrogen and phosphorus availability, greater accumulation of quality-related metabolites, higher antioxidant enzyme activities, and distinct rhizosphere microbial carbon-metabolic potential. These findings provide an integrated soil–microbiome–metabolome perspective for understanding propagation-related differences in tea quality.

Tea quality is primarily determined by the accumulation of specialized metabolites in fresh leaves; however, the mechanisms by which propagation methods influence tea quality through plant–soil–microbiome interactions remain poorly understood. Here, sexually propagated (SR) and asexually propagated (AR) tea plants were comparatively investigated by integrating soil physicochemical analyses, leaf physiological assessments, widely targeted metabolomics, and rhizosphere metagenomic profiling. Compared with AR, SR significantly improved soil nutrient availability, characterized by higher soil organic matter, nitrogen, and phosphorus contents, and promoted the accumulation of key quality-related components, including tea polyphenols and soluble sugars. In particular, tea polyphenol content increased by 58.8%, while available phosphorus increased by 161.5% under SR conditions. SR also exhibited enhanced antioxidant capacity, as evidenced by elevated superoxide dismutase, peroxidase, and indole-3-acetic acid oxidase activities while maintaining hydrogen peroxide homeostasis. Metabolomic analysis revealed distinct metabolic reprogramming between propagation types, with differential metabolites significantly enriched in flavonoid biosynthesis, phenolic acid metabolism, caffeine metabolism, and α-linolenic acid metabolism pathways. Concurrently, metagenomic analyses demonstrated that SR reshaped rhizosphere microbial communities by enriching Actinomycetota, Pseudomonadota, and Planctomycetota and altering microbial functional profiles associated with central carbon metabolism, including glycolysis and the tricarboxylic acid cycle. Integrated microbiome–metabolome analyses further revealed strong positive associations between SR-enriched microbial taxa and quality-related metabolites, particularly flavonoids and phenolic acids, suggesting a close coupling between rhizosphere microbial functions and leaf metabolic reprogramming. Collectively, our findings demonstrate that propagation method acts as an important driver of tea quality formation by coordinating soil nutrient availability, rhizosphere microbial carbon-cycling functions, plant physiological regulation, and metabolite accumulation. This study provides multi-omics evidence for a soil–microbiota–metabolome coupling mechanism underlying propagation method-dependent tea quality formation and highlights the potential of sexual propagation as a strategy for producing high-quality tea.