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.

Accurate and effective monitoring of environmental parameters in tea seedling greenhouses is an important basis for regulating the seedling environment, which is crucial for improving the seedling growth quality. This study proposes a tea seedling growth simulation (TSGS) model based on deep learning. The Internet of Things system was used to measure environmental change during the whole seedling process. The correlation between the environmental parameters and the biomass growth of tea seedlings in various varieties was analyzed. A CNN-LSTM network was proposed to build the TSGS model of light, temperature, water, gas, mineral nutrition, and growth biomass. The results showed that: (1) the average correlation coefficients of air temperature, soil temperature, and soil moisture with the biomass growth of tea seedlings were 0.78, 0.84, and −0.63, respectively, which were three important parameters for establishing the TSGS model. (2) For evaluating the TSGS model of a single variety, the accuracy of ZM’s TSGS based on the CNN-LSTM network was the highest (Rp² = 0.98, RMSEP = 0.14). (3) For evaluating the TSGS model of multiple varieties, the accuracy of TSGS based on the CNN-LSTM network was the highest (Rp² = 0.96, RMSEP = 0.17). This study provided effective technical parameters for intelligent control of tea-cutting growth and a new method for rapid breeding.

The physiological state of overwintering tea leaves is crucial for the growth and quality formation of spring tea shoots. Low temperatures in winter can easily cause damage to overwintering tea plants, leading to leaf chlorosis and abnormal physiological functions. Many pieces of research have shown that shading could promote chlorophyll (Chl) accumulation in tea leaves, but the impact on overwintering tea plants is not yet known. In this study, different shading rates (no-shading, S0%; 30% shading, S30%; 75% shading, S75%) were used to treat overwintering tea plants, which explored the effect of shading on the color and physiological functions of tea leaves. The results showed that Chl, carotenoid, and soluble sugar (SS) contents were S75% > S30% > S0%, and the net photosynthetic rate (Pn) was S75% > S30% > S0%. Transcriptome analysis showed that the genes involved in chlorophyll and carotenoid metabolism (such as protochlorophyllide reductase POR and zeaxanthin epoxidase ZEP) and photosynthesis (such as photosystem II P680 reaction center D2 protein PsbA and photosystem II CP47 chlorophyll apoprotein PsbB) were significantly up-regulated under shading. In addition, many differentially expressed genes (DEGs) were enriched in “starch and sucrose metabolism (ko00500)” and “anthocyanin biosynthesis (ko00942)” pathways. In summary, this study provided a theoretical basis and technical support for maintaining green leaves and normal physiological functions of overwintering tea plants.

The structure of plant communities and their response to temperature variations are an essential basis for evaluating the ecological structure and function of tea plantations. However, field surveys and quantitative evaluation of plant communities and eco-tea plantations remain challenging. In this study, a novel strategy was proposed for rapid surveillance of plant community structure and its response to changes in weather conditions in tea plantations. This strategy aims to construct the normalized tea plantation ecological index (NTEI) by synergizing environmental parameters with multi-source remote sensing data; establish the fitting and inversion model of NTEI by cascading the Fourier function with the convolutional neural networks-gate recurrent unit (CNN-GRU) network; and evaluate the variability of the plant community in tea plantations by analyzing the variation characteristics of the NTEI and the measured temperature. The study revealed that: (1) The NTEI can objectively characterize the plant communities of tea plantations, and its variation characteristics were consistent with the changes in vegetation phenology and temperature; (2) The Fourier function has the potential to quantify NTEI, and it's fitting R2 for the NTEI of 9 plant communities ranged from 0.840 to 0.921; (3) The CNN-GRU has the most advantage in establishing the prediction model of NTEI, and its prediction accuracy was Rp2=0.955, RMSEP=0.314; (4) The plant communities with high species richness increased regional ecological stability, had a strong buffering capacity against temperature changes, and had less variability in NTEI. The results provide significant guidance for building plant community structures and improving the ecological benefits of tea plantations.

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