Background: Nanotechnology holds revolutionary potential in the field of agriculture, with zinc oxide nanoparticles (ZnO NPs) demonstrating advantages in promoting crop growth. Enhanced photosynthetic efficiency is closely linked to improved vigor and superior quality in tea plants, complemented by the beneficial role of phyllosphere microorganisms in maintaining plant health. However, the effects of ZnO NPs on the photosynthesis of tea plants, the sprouting of new shoots, and the community of phyllosphere microorganisms have not been fully investigated. Results: This study investigated the photosynthetic physiological parameters of tea plants under the influence of ZnO NPs, the content of key photosynthetic enzymes such as RubisCO, chlorophyll content, chlorophyll fluorescence parameters, transcriptomic and extensive targeted metabolomic profiles of leaves and new shoots, mineral element composition in these tissues, and the epiphytic and endophytic microbial communities within the phyllosphere. The results indicated that ZnO NPs could enhance the photosynthesis of tea plants, upregulate the expression of some genes related to photosynthesis, increase the accumulation of photosynthetic products, promote the development of new shoots, and alter the content of various mineral elements in the leaves and new shoots of tea plants. Furthermore, the application of ZnO NPs was observed to favorably influence the microbial community structure within the phyllosphere of tea plants. This shift in microbial community dynamics suggests a potential for ZnO NPs to contribute to plant health and productivity by modulating the phyllosphere microbiome. Conclusion: This study demonstrates that ZnO NPs have a positive impact on the photosynthesis of tea plants, the sprouting of new shoots, and the community of phyllosphere microorganisms, which can improve the growth condition of tea plants. These findings provide new scientific evidence for the application of ZnO NPs in sustainable agricultural development and contribute to advancing research in nanobiotechnology aimed at enhancing crop yield and quality. Graphical

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.

Nε-Acetylation of lysine residues, a frequently occurring post-translational modification, plays important functions in regulating physiology and metabolism. However, the information of global overview of protein acetylome under nitrogen-starvation/resupply in tea (Camellia sinensis) leaves was limited. And the full function of lysine acetylated proteins of tea plants in nitrogen absorption and assimilation remains unclear. Here, we performed the global review of lysine acetylome in tea leaves under nitrogen (N)-starvation/resupply, using peptide prefractionation, immunoaffinity enrichment, and coupling with high sensitive LC-MS/MS combined with affinity purification analysis. Altogether, 2229 lysine acetylation sites on 1286 proteins were identified, of which 16 conserved motifs in E*KacK, Kac*K, Kac*R, Kac*HK, Kac*N, Kac*S, Kac*T, Kac*D, were extracted from 2180 acetylated peptides. Approximately, 36.76% of the acetylated lysines were located in the regions of ordered secondary structures. The most of the identified lysine acetylation proteins were located in the chloroplast (39%) and cytoplasm (29%). The largest group of acetylated proteins consisted of many enzymes, such as ATP synthase, ribosomal proteins and malate dehydrogenase [NADP], which were related to metabolism (38%) in the biological process. These acetylated proteins were mainly enriched in three primary protein complexes of photosynthesis: photosystem I, photosystem II and the cytochrome b6/f complex. And some acetylated proteins related to glycolysis and secondary metabolite biosynthesis were increased/decreased under N-resupply. Moreover, the PPI (protein-protein interaction) analysis revealed that the diverse interactions of identified acetylated proteins mainly involved in photosynthesis and ribosome. The results suggested that lysine acetylated proteins might play regulating roles in metabolic process in tea leaves. The critical regulatory roles mainly involved in diverse aspects of metabolic processes, especially in photosynthesis, glycolysis and secondary metabolism. A lot of proteins related to the photosynthesis and glycolysis were found to be acetylated, including LHCA1, LHCA3, LHCB6, psaE, psaD, psaN, GAPDH, PEPC, ENL and petC. And some proteins related to flavonoids were also found to be acetylated, including PAL, DFR, naringenin 3-dioxygenase and CHI. The provided data may serve as important resources for exploring the physiological, biochemical, and genetic role of lysine acetylation in tea plants. Data are available via ProteomeXchange with identifier PXD008931.

Aboveground biomass (AGB) and leaf area index (LAI) are important indicators to measure crop growth and development. Rapid estimation of AGB and LAI is of great significance for monitoring crop growth and agricultural site-specific management decision-making. As a fast and non-destructive detection method, unmanned aerial vehicle (UAV)-based imaging technologies provide a new way for crop growth monitoring. This study is aimed at exploring the feasibility of estimating AGB and LAI of mung bean and red bean in tea plantations by using UAV multispectral image data. The spectral parameters with high correlation with growth parameters were selected using correlation analysis. It was found that the red and near-infrared bands were sensitive bands for LAI and AGB. In addition, this study compared the performance of five machine learning methods in estimating AGB and LAI. The results showed that the support vector machine (SVM) and backpropagation neural network (BPNN) models, which can simulate non-linear relationships, had higher accuracy in estimating AGB and LAI compared with simple linear regression (LR), stepwise multiple linear regression (SMLR), and partial least-squares regression (PLSR) models. Moreover, the SVM models were better than other models in terms of fitting, consistency, and estimation accuracy, which provides higher performance for AGB (red bean: R² = 0.811, root-mean-square error (RMSE) = 0.137 kg/m², normalized RMSE (NRMSE) = 0.134; mung bean: R² = 0.751, RMSE = 0.078 kg/m², NRMSE = 0.100) and LAI (red bean: R² = 0.649, RMSE = 0.36, NRMSE = 0.123; mung bean: R² = 0.706, RMSE = 0.225, NRMSE = 0.081) estimation. Therefore, the crop growth parameters can be estimated quickly and accurately using the models established by combining the crop spectral information obtained by the UAV multispectral system using the SVM method. The results of this study provide valuable practical guidelines for site-specific tea plantations and the improvement of their ecological and environmental benefits.