Tea Shoots Research Articles

Influence of steaming duration, chlorophyll-a and -b content and ratio, and pH on the color of green tea processed from multiple tea (Camellia sinensis L.) cultivars.

The color of green tea is an important quality indicator. In recent years, shading of tea (Camellia sinensis L.) plants has been widely adopted for green tea production to enhance its green color and umami taste. In this study, we identified factors that influence green tea color by (i) examining variation in the chlorophyll content of fresh new tea shoots among cultivars, cropping seasons, and the degree of shading, (ii) investigating the rate of conversion of chlorophyll to pheophytin during the tea manufacturing process, specifically with steaming duration, and (iii) analyzing the effects of the new tea shoot properties and the steaming process on colorimetric values of the steamed new tea shoots. Multiple regression analysis revealed that three factors contributed to the rate of conversion of each chlorophyll type to pheophytin in steamed new tea shoots (ranked by importance): steaming duration > each chlorophyll type (chlorophyll-a and chlorophyll-b) content of fresh new tea shoots > pH. The colorimetric hue angle (h) value of steamed new tea shoots was influenced by four factors (ranked by importance): steaming duration > total chlorophyll (chlorophyll-a + chlorophyll-b) content in fresh new tea shoots > pH > chlorophyll-a/chlorophyll-b ratio in fresh new tea shoots. Differences in the color of new tea shoots can be explained by the aforementioned four factors. The findings will be useful for cultivar selection, and determining the appropriate degree of shading and steaming duration, to produce high-quality green teas with a good appearance. © 2024 Society of Chemical Industry.

Nitenpyram in tea: Eco-friendly detection methodology and residue behavior

Studies on nitenpyram determination and behavior within tea remain limited despite its widespread use as a neonicotinoid. An organic-saving analytical approach tailored for the detection of nitenpyram in tea was established. Nitenpyram was extracted by boiling water and cleaned up by Cleanert PCX solid-phase. The average recoveries were 75.1–94.5 %, with relative standard deviations (RSDs) of 0.7–8.6 % for saving 34.5–88.6 % organic solvent. The limits of quantification (LOQs) were 0.002 mg·kg−1 in fresh tea shoots, 0.005 mg·kg−1 in made tea, and 0.001 mg·L-1 in tea brew, satisfying the current minimum Maximum Residue Limit (MRL). Nitenpyram dissipated rapidly with half-lives of 1.2–1.4 days at the recommended dosage (27 g a.i. ha−1) in two locations. Remarkably, 20–110 % of nitenpyram was leached out from made tea in different brewing modes. This work provides insights into nitenpyram’s rational application in tea cultivation and offers considerations to institutions tasked with unestablished MRLs in tea.

Tea green leafhopper infestations affect tea plant growth by altering the synthesis of brassinolide.

Tea green leafhoppers are insects widely distributed in major tea-growing areas. At present, less attention has been paid to the study on effect of tea green leafhopper infestation on tea growth phenotype. In this study, tea green leafhoppers were used to treat tea branches in laboratory and co-treated with brassinolide (BL), the highest bioactivity of brassinosteroids (BRs), in tea garden. The results showed that the expression of genes related to BRs synthesis was inhibited and BL content was reduced in tea shoots after infestation by tea green leafhoppers. In addition, area of each leaf position, length and diameter of internodes, and the biomass of the tender shoots of tea plant were decreased after infestation by tea green leafhoppers. The number of trichomes, leaf thickness, palisade tissue thickness and cuticle thickness of tea shoots were increased after tea green leafhoppers infestation. BL spraying could partially recover the phenotypic changes of tea branches caused by tea green leafhoppers infestation. Further studies showed that tea green leafhoppers infestation may regulate the expression of CsDWF4 (a key gene for BL synthesis) through transcription factors CsFP1 and CsTCP1a, which finally affect the BL content. Moreover, BL was applied to inhibit the tea green leafhoppers infestation on tea shoots. In conclusion, our study revealed the effect of plant hormone BL-mediated tea green leafhoppers infestation on the growth phenotype of tea plants.

Changes and dynamics of the main quality components in tea leaves of 4 tea cultivars during the shading process

The purpose of this study was to analyze the relationship between different shading conditions, picking time for main quality components of tea. The main quality components of 4 tea cultivars [Camellia sinensis (L.) O. Kuntze], including the separated tea shoot tissues (leaves, buds, stems), under 4 shading rates during the traditional 20-day shading period were examined. Results showed the Chlorophyll contents after 12 days of shading treatment were significantly higher than control, but after that it ceased increasing significantly Initially, the theanine, total free amino acid, caffeine, and tea polyphenol contents showed decreased trend but then leveled off after 12 days. The increase of free amino acids, caffeine, chlorophyll and the decrease of tea polyphenols induced by shading were mainly determined by shading level and tenderness. Meanwhile, shading changed the proportion of main quality components in tea leaves and stems, long-term shading does not promote high quality matcha. All of the results indicated the quality of matcha with grade 1 could be produced by shading rate of 80 % and shading for 12 days. Appropriate shading time and shading rate are beneficial to improve the quality of Matcha.

The CsmiR397a-CsLAC17 module regulates lignin biosynthesis to balance the tenderness and gray blight resistance in young tea shoots.

Lignin accumulation can enhance the disease resistance of young tea shoots (Camellia sinensis). It also greatly reduces their tenderness, which indirectly affects the quality and yield of tea. Therefore, the regulation of lignin biosynthesis appears to be an effective way to balance tenderness and disease resistance in young tea shoots. In this study, we identified a laccase gene, CsLAC17, that is induced during tenderness reduction and gray blight infection in young tea shoots. Overexpression of CsLAC17 significantly increased the lignin content in transgenic Arabidopsis, enhancing their resistance to gray blight and decreasing stem tenderness. In addition, we found that CsLAC17 was negatively regulated by the upstream CsmiR397a by 5'-RLM-RACE, dual-luciferase assay, and transient expression in young tea shoots. Interestingly, the expression of CsmiR397a was inhibited during tenderness reduction and gray blight infection of young tea shoots. Overexpression of CsmiR397a reduced lignin accumulation, resulting in decreased resistance to gray blight and increased stem tenderness in transgenic Arabidopsis. Furthermore, the transient overexpression of CsmiR397a and CsLAC17 in tea leaves directly confirms the function of the CsmiR397a-CsLAC17 module in lignin biosynthesis and its effect on disease resistance. These results suggest that the CsmiR397a-CsLAC17 module is involved in balancing tenderness and gray blight resistance in young tea shoots by regulating lignin biosynthesis.

An RGB‐D object detection model with high‐generalization ability applied to tea harvesting robot for outdoor cross‐variety tea shoots detection

AbstractDetecting tea shoots is the first and most crucial step in achieving intelligent tea harvesting. However, when faced with thousands of tea varieties, establishing a high‐quality and comprehensive database comes with significant costs. Therefore, it has become an urgent challenge to improve the model's generalization ability and train it with minimal samples to develop a model capable of achieving optimal detection performance in various environments and tea varieties. This paper introduces a model named You Only See Tea (YOST) which utilizes depth maps to enhance model's generalization ability. It is applied to detect tea shoots in complex environments and to perform cross‐variety tea shoots detection. Our approach differs from common data augmentation strategies aimed at enhancing model generalization by diversifying the data set. Instead, we enhance the model's learning capability by strategically amplifying its attention towards core target features while simultaneously reducing attention towards noncore features. The proposed module YOST is developed upon the You Only Look Once version 7 (YOLOv7) model, utilizing two shared‐weight backbone networks to process both RGB and depth images. Then further integrate two modalities with feature layers at the same scale into our designed Ultra‐attention Fusion and Activation Module. By utilizing this approach, the model can proficiently detect targets by capturing core features, even when encountering complex environments or unfamiliar tea leaf varieties. The experimental results indicate that YOST displayed faster and more consistent convergence compared with YOLOv7 in training. Additionally, YOST demonstrated a 6.58% enhancement in AP50 for detecting tea shoots in complex environments. Moreover, when faced with a cross‐variety tea shoots detection task involving multiple unfamiliar varieties, YOST showcased impressive generalization abilities, achieving a significant maximum AP50 improvement of 33.31% compared with YOLOv7. These findings establish its superior performance. Our research departs from the heavy reliance on high‐generalization models on a large number of training samples, making it easier to train small‐scale, high‐generalization models. This approach significantly alleviates the pressure associated with data collection and model training.

YOLOX target detection model can identify and classify several types of tea buds with similar characteristics

Currently, the accuracy of tea bud identification is crucial in the intelligent development of the tea industry, and this is due to the fact that identifying tea buds is a key step in determining the quality of tea and distinguishing the categories. In this experiment, 3728 tea shoots with similar characteristics in four categories (Anji White Tea, Huangshan Seed, Longjing 43, and NongKang Early) were photographed to establish the dataset TBD (Tea Bud Dataset). In this experiment, we constructed a tea shoot recognition model. We used seven mainstream algorithms (YOLOv4, YOLOv5, YOLOX, YOLOv7, EfficientDet, Faster R-CNN and CenterNet) to conduct shoot recognition comparison experiments and found that the YOLOX algorithm performs the best with its Precision, Recall, F1 score, mAP 89.34%, 93.56%, 0.91, and 95.47%, respectively. Then the YOLOX algorithm combined with the dataset to construct the shoot recognition model, the shoots of four kinds of tea to establish a tea shoot classification model, the model to identify the Anji white tea shoots of Precision 76.19%, the yellow mountain species of Precision 90.54%, Longjing 43 Precision 80%, NongKang early to the morning of the Precision was 77.78%. The results of this experiment show that the established tea shoot classification model has achieved a better classification of the above four types of tea shoots, which can also understand the feasibility of mechanical intelligent tea picking and provide some theoretical support for the application of mechanical intelligent tea picking in practice.

CsMYB73 negatively regulates theanine accumulation mediated by CsGGT2 and CsGGT4 in tea shoots (Camellia sinensis).

Theanine metabolism is a necessary biological process during the planting and production of tea that determines tea quality. There is currently little knowledge about the transcriptional regulation of theanine metabolism in tea plants. In this study, we demonstrated that γ-glutamyl-transpeptidase CsGGT4, as a homologous protein of the theanine hydrolase CsGGT2, exhibited a higher theanine synthesis catalytic efficiency. Homology modeling and molecular docking showed that differential protein structures between CsGGT2 and CsGGT4 implied their different biological functions in tea plants. Theanine content correlated significantly with the expression of CsGGT2, CsGGT4 and the transcription factor CsMYB73 in tea shoots from different seasons. Additionally, CsMYB73 was confirmed to act as a nucleus-localized transcription factor (TF), directly interacts with the CsGGT2 and CsGGT4 promoters, serving as an activator of CsGGT2 and a suppressor of CsGGT4. Consequently, this leads to a negative association with theanine accumulation in tea shoots. Furthermore, the continuous increase in CsMYB73 produced a significantly increase in CsGGT2 expression and inhibited CsGGT4 expression. The present study reveals that the degradation of theanine has been observed to increase, concomitantly with the inhibition of theanine synthesis, resulting in a significant decline in the accumulation of theanine in tea shoots during the process of seasonal greening in 'Huangkui' leaves. This study contributes to the broader comprehension of the intricate transcriptional regulatory hierarchy that governs the metabolism of theanine in tea shoots, offering novel approaches for managing tea plantations and enhancing tea quality.

Integrated metabolomic and transcriptomic analyses reveal themolecular mechanism of amino acid transport between source and sink during tea shoot development.

The weighted gene co-expression network analysis and antisense oligonucleotide-mediated transient gene silencing revealed that CsAAP6 plays an important role in amino acid transport during tea shoot development. Nitrogen transport from source to sink is crucial for tea shoot growth and quality formation. Amino acid represents the major transport form of reduced nitrogen in the phloem between source and sink, but the molecular mechanism of amino acid transport from source leaves to new shoots is not yet clear. Therefore, the composition of metabolites in phloem exudates collected by the EDTA-facilitated method was analyzed through widely targeted metabolomics. A total of 326 metabolites were identified in the phloem exudates with the richest variety of amino acids and their derivatives (93), accounting for approximately 39.13% of the total metabolites. Moreover, through targeted metabolomics, it was found that the content of glutamine, glutamic acid, and theanine was the most abundant, and gradually increased with the development of new shoots. Meanwhile, transcriptome analysis suggested that the expression of amino acid transport genes changed significantly. The WGCNA analysis identified that the expression levels of CsAVT1, CsLHTL8, and CsAAP6 genes located in the MEterquoise module were positively correlated with the content of amino acids such as glutamine, glutamic acid, and theanine in phloem exudates. Reducing the CsAAP6 in mature leaves resulted in a significant decrease in the content of glutamic acid, aspartic acid, alanine, leucine, asparagine, glutamine, and arginine in the phloem exudates, indicating that CsAAP6 played an important role in the source to sink transport of amino acids in the phloem. The research results will provide the theoretical basis and genetic resources for the improvement of nitrogen use efficiency and tea quality.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

An improved YOLOv7 network using RGB-D multi-modal feature fusion for tea shoots detection

Due to the increasing scarcity of tea pickers, the implementation of intelligent harvesting for premium tea is a crucial prerequisite for the sustainable development of the premium tea industry. The initial step towards achieving intelligent and precise harvesting is the accurate detection of tender shoots, which consist of one bud and one leaf. However, accurately identifying tea shoots poses a challenging visual task due to their small size, variable shapes, as well as similar colors and backgrounds. The existing model, based on RGB images, can only detect partial targets. To address this issue and further enhance the detection of tea buds, this study proposes the utilization of multi-modal features encompassing red, green, blue, and depth (RGB-D) for identification. In addition, a unidirectional complementary multi-modal fusion method is introduced to minimize the adverse effects caused by low-quality depth information. Firstly, an RGB-D dataset comprising high-quality tea leaves is constructed, and the samples are carefully calibrated. Subsequently, an enhanced end-to-end RGB-D multi-modal object detection network, referred to as YOLO-RGBDtea, is developed based on You Only Look Once version 7 (YOLOv7). This model incorporates a parallel lightweight depth image feature extraction backbone network and incorporates a self-attention mechanism to prioritize contextual information. Lastly, a cross-modal spatial attention fusion module (CSFM) is devised to collaboratively integrate depth features with RGB features in a unidirectional manner. The experimental results reveal that YOLO-RGBDtea achieves an AP50 of 91.12% when confronting complex outdoor tea shoots, exhibiting significant performance improvements compared to YOLOv7, especially in scenarios involving small targets, overlapping target groups, and highly overexposed images. Notably, the parameter increment in YOLO-RGBDtea compared to the original YOLOv7 model is merely 17.8%, and the additional components can be seamlessly transferred to other models. Overall, this study introduces a straightforward yet effective multi-modal fusion method that bears theoretical and practical significance in advancing the detection of high-quality tea shoots in complex outdoor environments.

Continuous identification of the tea shoot tip and accurate positioning of picking points for a harvesting from standard plantations.

To address the current problems of large positioning error, low picking efficiency, and high cost of tea shoot picking, a continuous and precise harvesting scheme for tea shoots based on a two-dimensional (2D) perspective is designed in this study. A high-speed harvesting method for tea shoots in a standardized tea plantation assembly line type was proposed. First, a 2D view recognition model of tea shoot tips in a multi-disturbance environment was constructed, and accurate picking point coordinates were determined by combining a skeleton algorithm and curve growth. To avoid the losses of recognition accuracy caused by the mistaken clamping of blades and vibrations during harvester operations, accurate control of the harvester was realized by combining path planning and the S-curve speed control function. The recognition accuracy for the verification set of the recognition model was 99.9%, and the mean average precision (0.5:0.95) value was 0.97. The test results show that the error between the actual picking point position and the position determined by the model was within ± 3 mm, and the picking success rate was 83.6%. Therefore, we can realize fast and accurate picking of tea shoots and lay the foundation for continuous tea picking in the future by simplifying the identification and picking process.

Transcriptome and Biochemical Analyses of a Chlorophyll-Deficient Bud Mutant of Tea Plant (Camellia sinensis).

Tea leaf-color mutants have attracted increasing attention due to their accumulation of quality-related biochemical components. However, there is limited understanding of the molecular mechanisms behind leaf-color bud mutation in tea plants. In this study, a chlorina tea shoot (HY) and a green tea shoot (LY) from the same tea plant were investigated using transcriptome and biochemical analyses. The results showed that the chlorophyll a, chlorophyll b, and total chlorophyll contents in the HY were significantly lower than the LY's, which might have been caused by the activation of several genes related to chlorophyll degradation, such as SGR and CLH. The down-regulation of the CHS, DFR, and ANS involved in flavonoid biosynthesis might result in the reduction in catechins, and the up-regulated GDHA and GS2 might bring about the accumulation of glutamate in HY. RT-qPCR assays of nine DEGs confirmed the RNA-seq results. Collectively, these findings provide insights into the molecular mechanism of the chlorophyll deficient-induced metabolic change in tea plants.

Compositions and Antioxidant Activity of Tea Polysaccharides Extracted from Different Tea (Camellia sinensis L.) Varieties.

Tea polysaccharide (TPS) is a bioactive compound extracted from tea. It has raised great interest among researchers due to its bioactivity. However, few studies focused on the diversity of TPS in its compositions and antioxidant activity. This study collected 140 different tea varieties from four tea germplasm gardens in China, and their TPSs in tea shoots were extracted. The extraction efficiency, composition contents, including neutral sugar, uronic acid, protein, and tea polyphenols, and the scavenging abilities of hydroxyl radical (·OH) and superoxide radical (O2-·) of 140 TPSs were determined and analyzed. The results showed significant differences in the compositions and antioxidant activities of TPS extracted from different tea varieties. By applying hierarchical clustering analysis (HCA), we selected nine tea varieties with high TPS extraction efficiency and 26 kinds of TPS with high antioxidant capacity.

Exogenous Activation of the Ethylene Signaling Pathway Enhances the Freezing Tolerance of Young Tea Shoots by Regulating the Plant’s Antioxidant System

Tea plants (Camellia sinensis (L.) O. Kuntze) frequently suffer severe damage as a result of freezing temperatures in early spring, which severely affect tea quality and tea production in China. Emerging evidence has demonstrated that the ethylene signaling pathway plays an important role in tea plants’ freezing responses. However, how ethylene modulates the response to freezing in sprouting tea shoots is not clear. This study verified that the measurement of relative electrolyte leakage in young shoots after 1 h at −5 °C is a rapid way to evaluate their freezing tolerance in the laboratory. Further exploration of the mechanism involved in increasing tea-shoot freezing tolerance by monitoring changes in the transcription of ethylene-related genes and cold signaling-related genes, and the physiological and biochemical changes after the application of ethephon (2-chloroethylphosphonic acid, an ethylene release reagent), revealed that exogenous ethephon significantly increased the freezing tolerance of tea shoots within 3 days of treatment, while concomitantly altering the expression of the ethylene signaling pathway-related genes (i.e., CsETR1, CsETR2, and CsEBF1). Moreover, antioxidant enzyme activities, including superoxide dismutase, catalase, and peroxidase, were uniformly upregulated, which might constitute a major physiological change induced by ethylene signaling and may be responsible for the observed increase in freezing resistance. Nevertheless, soluble sugars and starch, trehalose metabolism, and cold signaling-related genes did not appear relevant to the freezing tolerance increase following ethephon application. This study demonstrated that the freezing tolerance of sprouting tea shoots can be rapidly increased by the exogenous activation of the ethylene signaling pathway and upregulation of the plant’s antioxidant system.

Real-time dense small object detection algorithm based on multi-modal tea shoots.

The difficulties in tea shoot recognition are that the recognition is affected by lighting conditions, it is challenging to segment images with similar backgrounds to the shoot color, and the occlusion and overlap between leaves. To solve the problem of low accuracy of dense small object detection of tea shoots, this paper proposes a real-time dense small object detection algorithm based on multimodal optimization. First, RGB, depth, and infrared images are collected form a multimodal image set, and a complete shoot object labeling is performed. Then, the YOLOv5 model is improved and applied to dense and tiny tea shoot detection. Secondly, based on the improved YOLOv5 model, this paper designs two data layer-based multimodal image fusion methods and a feature layerbased multimodal image fusion method; meanwhile, a cross-modal fusion module (FFA) based on frequency domain and attention mechanisms is designed for the feature layer fusion method to adaptively align and focus critical regions in intra- and inter-modal channel and frequency domain dimensions. Finally, an objective-based scale matching method is developed to further improve the detection performance of small dense objects in natural environments with the assistance of transfer learning techniques. The experimental results indicate that the improved YOLOv5 model increases the mAP50 value by 1.7% compared to the benchmark model with fewer parameters and less computational effort. Compared with the single modality, the multimodal image fusion method increases the mAP50 value in all cases, with the method introducing the FFA module obtaining the highest mAP50 value of 0.827. After the pre-training strategy is used after scale matching, the mAP values can be improved by 1% and 1.4% on the two datasets. The research idea of multimodal optimization in this paper can provide a basis and technical support for dense small object detection.

Transcriptome Revealed the Effect of Shading on the Photosynthetic Pigment and Photosynthesis of Overwintering Tea Leaves

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.

Tea green leafhopper-induced synomone attracts the egg parasitoids, mymarids to suppress the leafhopper.

The tea green leafhopper, Empoasca flavescens is the most important pest of tea plants in China. Mymarid attractants based on herbivore-induced plant volatiles (HIPVs) from leafhopper feeding and oviposition-induced plant volatiles (OIPVs) were formulated and tested as a novel pest control agent against the leafhopper in tea plantations. Results showed that two mymarid species, Stethynium empoascae and Schizophragma parvula, had a reducing effect on leafhopper populations. The HIPVs and OIPVs were identified and bioassayed to screen the key synomones showing strong attraction to the mymarids. They were formulated into different blends, of which Field Attractant 1, comprising linalool, methyl salicylate, (E)-2-hexenal, perillen and α-farnesene at ratio of 1:2:3:58:146 (20 mg/lure), showed the strongest attraction to the mymarids. In field trials with the attractant, the average parasitism rate (60.46 ± 23.71%) of tea leafhoppers by the two mymarids in the attractant-baited area was significantly higher than that (42.85 ± 19.24%) in the CK area. Also, the average leafhopper density (46 ± 30 per 80 tea shoots) in the attractant-baited area was significantly lower than that (110 ± 70 per 80 tea shoots) in the CK area. This study showed that a synthetic blend of key volatiles from HIPVs and OIPVs at an optimal ratio can be formulated into an attractant with the potential to attract and retain wild mymarid populations to suppress leafhopper populations in infested tea plantations, so as to reduce or avoid the spraying of insecticides. © 2023 Society of Chemical Industry.

An improved YOLOv5-based method for multi-species tea shoot detection and picking point location in complex backgrounds

Accurate detection of tea shoots and precise location of picking points are prerequisites for automated, intelligent and accurate tea picking. A method was developed for the detection of tea shoots and key points and the localisation of picking points in complex environments. Images of four types of tea shoots were collected from multiple fields of view in a tea plantation over two months and labelling criteria were established. The YOLO-Tea model was developed based on the YOLOv5 network model, which uses a content-based upsampling operator (CARAFE) with a larger field of perception to implement the tea shoot feature upsampling operation, adds a convolutional attention mechanism module (CBAM) to focus the model on both channel and spatial dimensions to detect and localise important areas of tea shoots in a large field of view. The Bottleneck Transformers module was used to inject global self-focus for residuals to create long-distance dependencies on the tea shot feature images, and a six-point landmark regression head was added. The experimental results demonstrated that the YOLO-Tea model improved the mean Average Precision (mAP) value of tea shoots and their key points by 5.26% compared to YOLOv5. Finally, we use image processing methods to locate picking point positions based on key point information during the model inference phase. This study has theoretical and practical implications for the detection of tea shoots and their key points, tea shoot alignment, phenotype identification, pose estimation and picking locations of premium teas in complex environments.

Method validation for detection of afidopyropen and M440I007 in tea.

Afidopyropen is a novel biorational insecticide for controlling piercing pests with great potential for application in tea gardens that can form the metabolite M440I007 when utilized for crops. However, because of a lack of analytical method for afidopyropen and M440I007 in tea, there is no means of monitoring the residues. Therefore, method development, validation and simultaneous determination of afidopyropen and M440I007 in fresh tea leaves, dried tea and tea infusion is of prime significance. A TPT cartridge-based method was developed for the solid phase extraction of afidopyropen and M440I007 from tea matrices. Extraction and clean-up conditions, including the composition, volume and temperature of elutions, were optimized to achieve the best results. Both targets were extracted using water and acetonitrile, with a water:acetonitrile (v/v) ratio of 4:10 for fresh leaves and 8:10 for dried tea, which were then cleaned and analyzed using ultraperformance liquid chromatography-tandem mass spectrometry. Both analytes demonstrated excellent linearity with a correlation coefficient above 0.998. The optimized analytical method offered limits of quantifications of 0.005, 0.005 and 0.002 mg kg-1 (converted to dried tea) in fresh tea shoots, dried tea and tea infusion for both targets, respectively. Average recoveries of afidopyropen and M440I007 ranged from 79.0% to 101.5%, with relative standard deviations ≤ 14.7%. The results showed that the method of determination for these insecticides in tea matrices was practical and efficient. © 2023 Society of Chemical Industry.