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Timely and accurate identification of tea tree pests is critical for effective tea tree pest control. We collected image data sets of eight common tea tree pests to accurately represent the true appearance of various aspects of tea tree pests. The dataset contains 782 images, each containing 1~5 different pest species randomly distributed. Based on this dataset, a tea garden pest detection and recognition model was designed using the Yolov7-tiny network target detection algorithm, which incorporates deformable convolution, the Biformer dynamic attention mechanism, a non-maximal suppression algorithm module, and a new implicit decoupling head. Ablation experiments were conducted to compare the performance of the models, and the new model achieved an average accuracy of 93.23%. To ensure the validity of the model, it was compared to seven common detection models, including Efficientdet, Faster Rcnn, Retinanet, DetNet, Yolov5s, YoloR, and Yolov6. Additionally, feature visualization of the images was performed. The results demonstrated that the Improved Yolov7-tiny model developed was able to better capture the characteristics of tea tree pests. The pest detection model proposed has promising application prospects and has the potential to reduce the time and economic cost of pest control in tea plantations.

Diseases and insect pests of tea leaves cause huge economic losses to the tea industry every year, so the accurate identification of them is significant. Convolutional neural networks (CNNs) can automatically extract features from images of tea leaves suffering from insect and disease infestation. However, photographs of tea tree leaves taken in a natural environment have problems such as leaf shading, illumination, and small-sized objects. Affected by these problems, traditional CNNs cannot have a satisfactory recognition performance. To address this challenge, we propose YOLO-Tea, an improved model based on You Only Look Once version 5 (YOLOv5). Firstly, we integrated self-attention and convolution (ACmix), and convolutional block attention module (CBAM) to YOLOv5 to allow our proposed model to better focus on tea tree leaf diseases and insect pests. Secondly, to enhance the feature extraction capability of our model, we replaced the spatial pyramid pooling fast (SPPF) module in the original YOLOv5 with the receptive field block (RFB) module. Finally, we reduced the resource consumption of our model by incorporating a global context network (GCNet). This is essential especially when the model operates on resource-constrained edge devices. When compared to YOLOv5s, our proposed YOLO-Tea improved by 0.3%–15.0% over all test data. YOLO-Tea’s AP0.5, APTLB, and APGMB outperformed Faster R-CNN and SSD by 5.5%, 1.8%, 7.0% and 7.7%, 7.8%, 5.2%. YOLO-Tea has shown its promising potential to be applied in real-world tree disease detection systems.

Tea diseases have a significant impact on the yield and quality of tea during the growth of tea trees. The shape and scale of tea diseases are variable, and the tea disease targets are usually small, with the intelligent detection processes of tea diseases also easily disturbed by the complex background of the growing region. In addition, some tea diseases are concentrated in the entire area of the leaves, needing to be inferred from global information. Common target detection models are difficult to solve these problems. Therefore, we proposed an improved tea disease detection model called TSBA-YOLO. We use the dataset of tea diseases collected at the Maoshan Tea Factory in China. The self-attention mechanism was used to enhance the ability of the model to obtain global information on tea diseases. The BiFPN feature fusion network and adaptively spatial feature fusion (ASFF) technology were used to improve the multiscale feature fusion of tea diseases and enhance the ability of the model to resist complex background interference. We integrated the Shuffle Attention mechanism to solve the problem of difficult identifications of small-target tea diseases. In addition, we used data-enhancement methods and transfer learning to expand the dataset and relocate the parameters learned from other plant disease datasets to enhance tea diseases detection. Finally, SIoU was used to further improve the accuracy of the regression. The experimental results show that the proposed model is good at solving a series of problems encountered in the intelligent recognition of tea diseases. The detection accuracy is ahead of the mainstream target detection models, and the detection speed reaches the real-time level.

Background The common tea tree disease known as “tea coal disease” ( Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification. Results Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging. Conclusions This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.

The root-associated microbiome significantly influences plant health and pest resistance, yet the temporal dynamics of its compositional and functional change in response to Ectropis grisescens Warren (Lepidoptera: Geometridae) infestation remain largely unexplored. The study took samples of leaves, roots, and rhizosphere soil at different times after the plants were attacked by E. grisescens. These samples were analyzed using transcriptomic and high-throughput sequencing of 16S rRNA techniques. The goal was to understand how the plant’s defense mechanisms and the microbial community around the roots changed after the attack. Additionally, bacterial feedback assays were conducted to evaluate the effects of selected microbial strains on plant growth and pest defense responses. By conducting 16S rRNA sequencing on the collected soil samples, we found significant shifts in bacterial communities by the seventh day, suggesting a lag in community adaptation. Transcriptomic analysis revealed that E. grisescens attack induced reprogramming of the tea root transcriptome, upregulating genes related to defensive pathways such as phenylpropanoid and flavonoid biosynthesis. Metagenomic data indicated functional changes in the rhizosphere microbiome, with enrichment in genes linked to metabolic pathways and nitrogen cycling. Network analysis showed a reorganization of core microbial members, favoring nitrogen-fixing bacteria like Burkholderia species. Bacterial feedback assays confirmed that selected strains, notably Burkholderia cepacia strain ABC4 (T1) and a nine-strain consortium (T5), enhanced plant growth and defense responses, including elevated levels of flavonoids, polyphenols, caffeine, jasmonic acid, and increased peroxidase (POD) and superoxide dismutase (SOD) activities. This study emphasizes the potential of utilizing root-associated microbial communities for sustainable pest management in tea cultivation, thereby enhancing resilience in tea crops while maintaining ecosystem balance.

Background Tea is one of the most widely consumed beverages in the world, with significant economic and cultural value. However, tea production faces many challenges due to various biotic and abiotic stresses, among which fungal diseases are particularly devastating. Results To understand the identity and pathogenicity of isolates recovered from tea plants with symptoms of wilt, phylogenetic analyses and pathogenicity assays were conducted. Isolates were characterized to the species level by sequencing the ITS, tef-1α , tub2 and rpb2 sequences and morphology. Four Fusarium species were identified: Fusarium fujikuroi , Fusarium solani , Fusarium oxysporum , and Fusarium concentricum . The pathogenicity of the Fusarium isolates was evaluated on 1-year-old tea plants, whereby F. fujikuroi OS3 and OS4 strains were found to be the most virulent on tea. Conclusions To the best of our knowledge, this is the first report of tea rot caused by F. fujikuroi in the world. This provides the foundation for the identification and control of wilt disease in tea plants.

The accurate identification of tea tree pests is crucial for tea production, as it directly impacts yield and quality. In natural tea garden environments, identifying pests is challenging due to their small size, similarity in color to tea trees, and complex backgrounds. To address this issue, we propose TTPRNet, a multi-scale recognition model designed for real tea garden environments. TTPRNet introduces the ConvNext architecture into the backbone network to enhance the global feature learning capabilities and reduce the parameters, and it incorporates the coordinate attention mechanism into the feature output layer to improve the representation ability for different scales. Additionally, GSConv is employed in the neck network to reduce redundant information and enhance the effectiveness of the attention modules. The NWD loss function is used to focus on the similarity between multi-scale pests, improving recognition accuracy. The results show that TTPRNet achieves a recall of 91% and a mAP of 92.8%, representing 7.1% and 4% improvements over the original model, respectively. TTPRNet outperforms existing object detection models in recall, mAP, and recognition speed, meeting real-time requirements. Furthermore, the model integrates a counting function, enabling precise tallying of pest numbers and types and thus offering practical solutions for accurate identification in complex field conditions.

(1) Background: In recent years, new pests have been constantly emerging in tea trees, posing a significant threat to tea production. Therefore, it is necessary to monitor and investigate whether new pests have emerged in tea trees. (2) Methods: A new tea pest discovered in a tea garden was identified through mitochondrial cytochrome-c oxidase subunit I (COI) gene sequence analysis and observation of morphological characteristics. Its occurrence pattern was also analyzed in detail, and preliminary control methods were proposed. (3) Results: During the 2023 tea garden pest investigation, we discovered a new tea pest for the first time in a tea garden in Jiepai Town, Hengyang County, Hengyang City, Hunan Province, and identified it as Nysius ericae (Schilling). The results indicated that N. ericae was mainly fed on the upper leaves of tea trees, and high temperature and drought were suitable for its occurrence. Furthermore, various concentrations (1~16 mg/L) of matrine showed significant toxicity against N. ericae under laboratory conditions. (4) Conclusions: Our research has discovered for the first time a new pest of tea trees, providing an important scientific foundation for the monitoring, early warning, and prevention and control of N. ericae in tea gardens, which is of great significance for ensuring the ecological security and tea quality of tea gardens.

Background Flowers of Chrysanthemum × morifolium Ramat. are used as tea in traditional Chinese cuisine. However, with increasing population and urbanization, water and land availability have become limiting for chrysanthemum tea production. Hydroponic culture enables effective, rapid nutrient exchange, while requiring no soil and less water than soil cultivation. Hydroponic culture can reduce pesticide residues in food and improve the quantity or size of fruits, flowers, and leaves, and the levels of active compounds important for nutrition and health. To date, studies to improve the yield and active compounds of chrysanthemum have focused on soil culture. Moreover, the molecular effects of hydroponic and soil culture on chrysanthemum tea development remain understudied. Results Here, we studied the effects of soil and hydroponic culture on yield and total flavonoid and chlorogenic acid contents in chrysanthemum flowers ( C. morifolium ‘wuyuanhuang’). Yield and the total flavonoids and chlorogenic acid contents of chrysanthemum flowers were higher in the hydroponic culture system than in the soil system. Transcriptome profiling using RNA-seq revealed 3858 differentially expressed genes (DEGs) between chrysanthemum flowers grown in soil and hydroponic conditions. Gene Ontology (GO) enrichment annotation revealed that these differentially transcribed genes are mainly involved in “cytoplasmic part”, “biosynthetic process”, “organic substance biosynthetic process”, “cell wall organization or biogenesis” and other processes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed enrichment in “metabolic pathways”, “biosynthesis of secondary metabolites”, “ribosome”, “carbon metabolism”, “plant hormone signal transduction” and other metabolic processes. In functional annotations, pathways related to yield and formation of the main active compounds included phytohormone signaling, secondary metabolism, and cell wall metabolism. Enrichment analysis of transcription factors also showed that under the hydroponic system, bHLH, MYB, NAC, and ERF protein families were involved in metabolic pathways, biosynthesis of secondary metabolites, and plant hormone signal transduction. Conclusions Hydroponic culture is a simple and effective way to cultivate chrysanthemum for tea production. A transcriptome analysis of chrysanthemum flowers grown in soil and hydroponic conditions. The large number of DEGs identified confirmed the difference of the regulatory machinery under two culture system.

Background Shoot branching is one of the important agronomic traits affecting yields and quality of tea plant ( Camellia sinensis ). Cytokinins (CTKs) play critical roles in regulating shoot branching. However, whether and how differently alternative splicing (AS) variant of CTKs-related genes can influence shoot branching of tea plant is still not fully elucidated. Results In this study, five AS variants of CTK biosynthetic gene adenylate isopentenyltransferase ( CsA-IPT5 ) with different 3′ untranslated region (3ˊ UTR) and 5ˊ UTR from tea plant were cloned and investigated for their regulatory effects. Transient expression assays showed that there were significant negative correlations between CsA-IPT5 protein expression, mRNA expression of CsA-IPT5 AS variants and the number of ATTTA motifs, respectively. Shoot branching processes induced by exogenous 6-BA or pruning were studied, where CsA-IPT5 was demonstrated to regulate protein synthesis of CsA-IPT5, as well as the biosynthesis of trans -zeatin ( t Z)- and isopentenyladenine (iP)-CTKs, through transcriptionally changing ratios of its five AS variants in these processes. Furthermore, the 3′ UTR AS variant 2 (3AS2) might act as the predominant AS transcript. Conclusions Together, our results indicate that 3AS2 of the CsA-IPT5 gene is potential in regulating shoot branching of tea plant and provides a gene resource for improving the plant-type of woody plants.