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Abstract To address the issues of low accuracy and slow response speed in tea disease classification and identification, an improved YOLOv7 lightweight model was proposed in this study. The lightweight MobileNeXt was used as the backbone network to reduce computational load and enhance efficiency. Additionally, a dual-layer routing attention mechanism was introduced to enhance the model’s ability to capture crucial details and textures in disease images, thereby improving accuracy. The SIoU loss function was employed to mitigate missed and erroneous judgments, resulting in improved recognition amidst complex image backgrounds.The revised model achieved precision, recall, and average precision of 93.5%, 89.9%, and 92.1%, respectively, representing increases of 4.5%, 1.9%, and 2.6% over the original model. Furthermore, the model’s volum was reduced by 24.69M, the total param was reduced by 12.88M, while detection speed was increased by 24.41 frames per second. This enhanced model efficiently and accurately identifies tea disease types, offering the benefits of lower parameter count and faster detection, thereby establishing a robust foundation for tea disease monitoring and prevention efforts.

To explore the effects of different withering methods on the quality of Congou black tea, this study focused on five different withering methods: natural withering, warm-air withering, sun–natural combined withering, sun withering, and shaking withering. Gas chromatography‒mass spectrometry (GC‒MS), high-performance liquid chromatography (HPLC), and ion-exchange chromatography techniques were used to analyze the nonvolatile and volatile components and composition of the tea. The results revealed significant differences (p < 0.05) in the contents of nonvolatile constituents including caffeine, polyphenols, soluble sugars, free amino acids and their components, theaflavins, thearubigins, and catechins among the five different withering methods, with varying degrees of correlation between these components. A total of 227 aroma compounds were detected, and significant differences in the contents of alcohols, aldehydes, and ketones were observed. A relative odor activity value (ROVA) analysis of the aroma compounds revealed that 19 compounds had an ROVA > 1. Among them, benzylaldehyde, trans-2-decenal, decanal, benzaldehyde, nonanal, hexanal, trans-linalool, and geraniol from the shaking withering method had significantly higher ROVA values than those from the other withering methods, which may be the reason for the prominent floral and fruity aroma of shaking withering. This study revealed the impact of different withering methods on the quality of Congou black tea, providing a scientific basis for the development of Congou black tea with different flavors and the improvement of Congou black tea processing techniques.

China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science.

IntroductionIn order to solve the problem of precise identification and counting of tea pests, this study has proposed a novel tea pest identification method based on improved YOLOv7 network.MethodsThis method used MPDIoU to optimize the original loss function, which improved the convergence speed of the model and simplifies the calculation process. Replace part of the network structure of the original model using Spatial and Channel reconstruction Convolution to reduce redundant features, lower the complexity of the model, and reduce computational costs. The Vision Transformer with Bi-Level Routing Attention has been incorporated to enhance the flexibility of model calculation allocation and content perception.ResultsThe experimental results revealed that the enhanced YOLOv7 model significantly boosted Precision, Recall, F1, and mAP by 5.68%, 5.14%, 5.41%, and 2.58% respectively, compared to the original YOLOv7. Furthermore, when compared to deep learning networks such as SSD, Faster Region-based Convolutional Neural Network (RCNN), and the original YOLOv7, this method proves to be superior while being externally validated. It exhibited a noticeable improvement in the FPS rates, with increments of 5.75 HZ, 34.42 HZ, and 25.44 HZ respectively. Moreover, the mAP for actual detection experiences significant enhancements, with respective increases of 2.49%, 12.26%, and 7.26%. Additionally, the parameter size is reduced by 1.39 G relative to the original model.DiscussionThe improved model can not only identify and count tea pests efficiently and accurately, but also has the characteristics of high recognition rate, low parameters and high detection speed. It is of great significance to achieve realize the intelligent and precise prevention and control of tea pests.

The 6-DOF Stewart parallel elevation platform serves as the platform for mounting the tea-picking robotic arm, significantly impacting the operational scope, velocity, and harvesting precision of the robotic arm. Utilizing the Stewart setup, a parallel elevation platform with automated lifting and leveling capabilities was devised, ensuring precise halts at designated elevations for seamless harvesting operations. The effectiveness of the platform parameter configuration and the reasonableness of the posture changes were verified. Firstly, the planting mode and growth characteristics of Yunnan large-leaf tea trees were analyzed to determine the preset path, posture changes, and mechanism stroke of the Stewart parallel lifting platform, thereby determining the basic design specifications of the platform. Secondly, a 3D model was established using SolidWorks, a robust adaptive PD control model was built using MATLAB for simulation, and dynamic calculations were carried out through data interaction in Simulink and ADAMS. Finally, the rationality of the lifting platform design requirements was determined based on simulation data, a 6-DOF Stewart parallel lifting platform was manufactured, and a motion control system was built for experimental verification according to the design specifications and simulation data. The results showed that the maximum deviation angle around the X, Y, and Z axes was 10°, the maximum lifting distance was 15 cm, the maximum load capacity was 60 kg, the platform response error was within ±0.1 mm, and the stable motion characteristics reached below the millimeter level, which can meet the requirements of automated operation of the auxiliary picking robotic arm.

To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine−learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single−factor and multi−factor comparative analyses, as well as for model validation, and the goodness−of−fit was assessed using the Akaike information criterion (AIC). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6‰, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9‰, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors (p < 0.05), the AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun−dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun−dried green tea under abiotic stress.

To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions.

To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

Lightning rods have been developed to prevent damage caused by lightning to organisms. However, the biological effect of the current transmitted into the soil through lightning rods is unknown. In this study, we analyzed the effects of lightning on soil properties, the microbial community, and the active components of Pu-erh tea (Camellia sinensis var. assamica) near lightning rods. The results showed that the contents of organic matter and available potassium, copper, and calcium in rhizosphere soil near the lightning rod were significantly higher than those in control soil (P < 0.05), while the contents of total potassium, phosphorus, iron, magnesium, and aluminum decreased. Lightning significantly increased the bacterial diversity of Pu-erh rhizosphere soil compared to control soil samples (P < 0.05). Sphingomonas, Nitrospira, and Reyranella were significantly enriched in soil samples near the lightning rod compared to soil samples far from the lightning rod. Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that adenosine/AMP kinase, chitodextrinase, flavorubredoxin, nucleotide metabolism, and carbohydrate digestion and absorption were significantly enriched in the rhizosphere soil samples near the lightning rod compared to the control samples (P < 0.05). β diversity analysis indicated the grounding of the lightning rod contributed to the community differentiation of rhizosphere bacteria. Amino acids, polyphenols, and soluble sugar increased in Pu-erh tea near the lightning rod, while the contents of catechin and anthocyanin decreased in Pu-erh tea near the lightning rod compared with the control sample (P < 0.05). Significant correlations were found among microbial indicators, soil properties, and Pu 'er tea components. This study serves as the first report on the effects of lightning rods on soil properties, microecology, and plant metabolism, which promotes the understanding of the biological effects of lightning, and provides a reference for the rational use of lightning resources.

In order to solve the problem of an accurate recognition of tea picking through tea picking robots, an edge device detection method is proposed in this paper based on ShuffleNetv2-YOLOv5-Lite-E for tea with one bud and two leaves. This replaces the original feature extraction network by removing the Focus layer and using the ShuffleNetv2 algorithm, followed by a channel pruning of YOLOv5 at the neck layer head, thus achieving the purpose of reducing the model size. The results show that the size of the improved generated weight file is 27% of that of the original YOLOv5 model, and the mAP value of ShuffleNetv2-YOLOv5-Lite-E is 97.43% and 94.52% on the pc and edge device respectively, which are 1.32% and 1.75% lower compared to that of the original YOLOv5 model. The detection speeds of ShuffleNetv2-YOLOv5-Lite-E, YOLOv5, YOLOv4, and YOLOv3 were 8.6 fps, 2.7 fps, 3.2 fps, and 3.4 fps respectively after importing the models into an edge device, and the improved YOLOv5 detection speed was 3.2 times faster than that of the original YOLOv5 model. Through the detection method, the size of the original YOLOv5 model is effectively reduced while essentially ensuring recognition accuracy. The detection speed is also significantly improved, which is conducive to the realization of intelligent and accurate picking for future tea gardens, laying a solid foundation for the realization of tea picking robots.

Chemically functionalized graphene oxide-based pn junction diodes have potential for future electronic device applications. Here, the authors report an all carbon pn diode with graphene oxide and carbon nanotubes electrodes showing excellent current rectification and efficient logic gates.

In this work, we have demonstrated a different effect on Fe1+σTe0.5Se0.5 and β-FeSe by changing the quenching temperature. Tc is clearly reduced in Fe1+σTe0.5Se0.5 after increasing the quenching temperature from 300 °C to 500 °C, while that of β-FeSe is almost unchanged. Structure refinement indicates that after quenched at 500 °C, FeTe4 tetrahedron exhibits an expansion with the stretched Fe-Te bond, together with the increased amount of interstitial iron. These particular changes on structure are believed to be responsible for the suppression of superconductivity in Fe1+σTe0.5Se0.5.

The occurrence of accidents in college and university chemical laboratories has raised significant concerns in society, emphasizing the need for effective safety education for students. In response, a virtual simulation teaching system for safety education in chemical laboratories has been developed, incorporating 3D visualization, network communication, multimedia, and human-computer interaction. This system transforms "typical accident cases", "hidden trouble investigations", and "accident response and evacuation" scenarios in different chemistry laboratory settings (including inorganic chemistry, organic chemistry, analytical chemistry, and physical chemistry) into interactive virtual simulation experiments. These simulations provide students with a highly realistic virtual experimental environment, enabling them to effectively learn how to respond to safety incidents such as fires, chemical burns, gas leaks, and electric shocks. By combining virtual and real knowledge, this teaching approach enhances students' engagement, eliminates the risks and costs associated with practical training, and improves their safety skills and problem-solving abilities. Ultimately, this course achieves the objectives of safety education and aligns with the educational policy of "three complete education". [ABSTRACT FROM AUTHOR]

Layered metallic transition metal dichalcogenides (TMDs) with bonding-free surfaces and weak Fermi-level pinning properties have exhibited potential application as electrode materials in two-dimensional (2D) semiconductor devices. However, various TMDs metallic materials are still limited in certain areas, especially due to the lack of a low-work function (<4.5 eV) counterpart. Herein, we report a polytelluride flux growth method for TiTe2 single crystals and determine the work function of this IVB group metallic TMD for the first time. The obtained crystal adopts a typical 1T-type structure (P...m1 space group, no. 164) with the lattice parameters a = 3.7668(4) Å and c = 6.4918(10) Å. The TiTe2 crystal showed metallic behavior with decreased resistivity upon cooling and a relatively low resistivity value (4.39 x 10-4 Ω cm) at room temperature. By ultraviolet photoelectron spectroscopy, the work function of TiTe2 was determined as 4.31 eV, which is much smaller than the reported data of other metallic TMDs. Our study demonstrates the interesting work function data of 1T-TiTe2, which can be utilized to make excellent metal-semiconductor contacts in 2D semiconductor devices. [ABSTRACT FROM AUTHOR]