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In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML) algorithms, this study presents an innovative approach for classifying the quality grade of fresh tea leaves. This approach leverages an integration of image recognition and deep learning (DL) algorithm to accurately classify tea leaves' grades by identifying distinct bud and leaf combinations. The method begins by acquiring separate images of orderly scattered and randomly stacked fresh tea leaves. These images undergo data augmentation techniques, such as rotation, flipping, and contrast adjustment, to form the scattered and stacked tea leaves datasets. Subsequently, the YOLOv8x model was enhanced by Space pyramid pooling improvements (SPPCSPC) and the concentration-based attention module (CBAM). The established YOLOv8x-SPPCSPC-CBAM model is evaluated by comparing it with popular DL models, including Faster R-CNN, YOLOv5x, and YOLOv8x. The experimental findings reveal that the YOLOv8x-SPPCSPC-CBAM model delivers the most impressive results. For the scattered tea leaves, the mean average precision, precision, recall, and number of images processed per second rates of 98.2%, 95.8%, 96.7%, and 2.77, respectively, while for stacked tea leaves, they are 99.1%, 99.1%, 97.7% and 2.35, respectively. This study provides a robust framework for accurately classifying the quality grade of fresh tea leaves. [ABSTRACT FROM AUTHOR]

As the raw material for tea making, the quality of tea leaves directly affects the quality of finished tea. The quality of fresh tea leaves is mainly assessed by manual judgment or physical and chemical testing of the content of internal components. Physical and chemical methods are more mature, and the test results are more accurate and objective, but traditional chemical methods for measuring the biochemical indexes of tea leaves are time-consuming, labor-costly, complicated, and destructive. With the rapid development of imaging and spectroscopic technology, spectroscopic technology as an emerging technology has been widely used in rapid non-destructive testing of the quality and safety of agricultural products. Due to the existence of spectral information with a low signal-to-noise ratio, high information redundancy, and strong autocorrelation, scholars have conducted a series of studies on spectral data preprocessing. The correlation between spectral data and target data is improved by smoothing noise reduction, correction, extraction of feature bands, and so on, to construct a stable, highly accurate estimation or discrimination model with strong generalization ability. There have been more research papers published on spectroscopic techniques to detect the quality of tea fresh leaves. This study summarizes the principles, analytical methods, and applications of Hyperspectral imaging (HSI) in the nondestructive testing of the quality and safety of fresh tea leaves for the purpose of tracking the latest research advances at home and abroad. At the same time, the principles and applications of other spectroscopic techniques including Near-infrared spectroscopy (NIRS), Mid-infrared spectroscopy (MIRS), Raman spectroscopy (RS), and other spectroscopic techniques for non-destructive testing of quality and safety of fresh tea leaves are also briefly introduced. Finally, in terms of technical obstacles and practical applications, the challenges and development trends of spectral analysis technology in the nondestructive assessment of tea leaf quality are examined. [ABSTRACT FROM AUTHOR]

To address the problem of a lack of accurate parameters in the discrete element simulation study of the machine-picked fresh tea leaf mechanized-sorting process, this study used machine-picked fresh tea leaves as the research object, established discrete element models of different fresh tea leaf components in EDEM software version 7.0.0. based on the bonded particle model using three-dimensional scanning inverse-modeling technology, and calibrated the simulation parameters through physical tests and virtual simulation tests. Firstly, the intrinsic parameters of machine-picked tea leaves were measured using physical tests; the physical-stacking tea leaf test was conducted using the cylinder lifting method, the tea leaf repose angle being 32.62° as measured from the stacking images using CAD. With the physical repose angle as the target value, the Plackeet–Burman test, the steepest-ascent test and the Box–Behnken optimization test were conducted in turn, and the results showed that the static friction coefficient between tea leaves, the rolling friction coefficient between tea leaves and the static friction coefficient between tea leaves and PVC have a major effect on the repose angle, and the optimal combination of the three significant parameters was determined. Finally, five simulations were conducted using the optimal combination of parameters, the relative error between the repose angle measured by the simulation test and the physical repose angle being just 0.28%. Moreover, the t-test obtained p > 0.05, indicating that there was no significant difference between the simulation test results and the physical test results. The results showed that the calibrated discrete element simulation parameters obtained could provide a reference for the discrete element simulation study of fresh tea leaves. [ABSTRACT FROM AUTHOR]

Tea polyphenols are considered as an important indicator of tea quality. Rapid detection of tea polyphenol content plays a valuable role for tea breeding and quality inspection during tea production. In this work, a portable rapid non-destructive detection device of tea polyphenols in fresh tea leaves was developed, which integrated the fusion technology of visible/short-wave (400–1050 nm) and long-wave (1000–1650 nm) near-infrared spectroscopy (Vis/NIR). Experimental results indicated that the spectra within the overlapping region (1000–1050 nm) were assembled by applying the spectral data fusing method. Followed by spectral data preprocessing with the Savitzky–Golay smoothing (SG) method, least squares support vector regression (LS–SVR) models were established for detecting the tea polyphenol content of fresh tea leaves. Based on the fused Vis/NIR spectra (dual-band), the correlation coefficient of calibration (RC), root mean square error of calibration (RMSEC), correlation coefficient of prediction (RP), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) reached 0.976, 0.679%, 0.893, 0.897%, and 2.230, respectively, which were better than the visible/short-wave or long-wave near infrared spectral data (single-band). The sensitive spectral wavebands of tea polyphenols extracted using the random frog (RF) algorithm were distributed in 402–448 nm, 555–600 nm, 810–1042 nm, 1056–1103 nm, 1219–1323 nm, 1406–1416 nm, and 1499–1511 nm. This demonstrated that the prediction of tea polyphenol content using fused spectral data combined with the LS–SVR model depended on various functional groups such as auxochromes, chromogenic groups, and hydrogen-containing groups. The proposed device is capable of non-destructive detection of tea polyphenol content in fresh tea leaves, which can provide effective technical support for tea breeding and tea leaf quality control. [ABSTRACT FROM AUTHOR]

Background: High‐quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking costs. The present study proposes a method based on an improved genetic algorithm for identifying fresh tea leaves in high‐speed parabolic motion using the phenotypic characteristics of the leaves. During parabolic flight, light is transmitted through the tea leaves, and six types of fresh tea leaves can be quickly identified by a camera. Results: The influence of combinations of morphology, color, and custom corner‐point morphological features on the classification results were investigated, and the necessary dimensionality of the model was tested. After feature selection and combination, the classification performance of the Naive Bayes, k‐nearest neighbor, and support vector machine algorithms were compared. The recognition time of Naive Bayes was the shortest, whereas the accuracy of support vector machine had the best classification accuracy at approximately 97%. The support vector machine algorithm with only three feature dimensions (equivalent diameter, circularity, and skeleton endpoints) can meet production requirements with an accuracy rate reaching 92.5%. The proposed algorithm was tested by using the Swedish leaf and Flavia data sets, on which it achieved accuracies of 99.57% and 99.44%, respectively, demonstrating the flexibility and efficiency of the recognition scheme detailed in the present study. Conclusion: This research provides an efficient tea leaves recognition system that can be applied to production lines to reduce manual picking costs. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This paper applies near-infrared spectroscopy (NIRS) and multiple chemometrics to efficiently distinguish the origins of fresh tea leaves. The key components were obtained using the partial least squares discriminant analysis (PLS-DA) method. PLS, synergy interval PLS (siPLS), principal component analysis (PCA), genetic algorithm (GA), and their combination methods were used to establish NIRS non-destructive discrimination models. Then, the practical application was examined using external samples. The study identified nine key components (variable importance for the projection (VIP) > 1): epigallocatechin, epicatechin, total sugar, water extracts, total catechins, gallocatechin gallate, tea polyphenols, gallocatechin, and epigallocatechin gallate. Of the six NIRS models, the siPLS-GA model that used 37 spectral data points produced the best results (Rp2 = 0.9706, RMSEP = 0.0772, RPD = 6.59). This model had a prediction accuracy of 96.67% for the prediction set samples and 93.33% for the external samples. It offers a rapid, precise, and non-invasive approach to monitor and regulate the illicit trade of fresh tea leaves, thereby guaranteeing the authenticity of Enshi Yulu products from the processing source and fostering the long-term prosperity and stability of the Enshi Yulu tea industry. • There is a limited amount of research on using NIRS to trace the origin of fresh tea leaves. • PLS-DA was used to identify the origins of fresh tea leaves based on nine key components. • The NIRS-siPLS-GA digital combination model exhibits high robustness and wide adaptability. • It is a low-cost modeling solution effectively addresses the authenticity problem of Enshi Yulu. • The research findings suggest broad application prospects. [ABSTRACT FROM AUTHOR]

• Green tea catechins and caffeine concentrations are affected by variety and leaf position. • The spectral differences can clearly distinguish varieties and leaf positions. • Quantitative determination models obtain good results with characteristic wavelengths. • The five major constituents in tea leaves can be rapidly determined by Vis-NIR spectroscopy. Catechin polyphenols and caffeine play an important role in tea quality. This study analyzed the visible and near-infrared (Vis-NIR) spectral variation and concentration differences of catechin and caffeine of fresh tea leaves (Camellia sinensis L.) in three varieties and six leaf positions, and a universal spectral model for quickly and accurately measurement of the catechin and caffeine content in the various varieties and leaf positions was established. It was found that all the catechins and caffeine were significantly influenced by variety and leaf position. While, the Vis/NIR spectrum as an indication of internal biochemical substances was successfully used to distinguish different varieties and leaf positions with the discrimination accuracy rates of 98.15% and 100%, respectively. The quantitative relationship between the chemical components and the data obtained by Vis-NIR spectroscopy was established based on multivariate regression analysis such as partial least squares (PLS) and multiple linear regression (MLR). Furthermore, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to select the characteristic wavelengths for the development of simple models. Results showed that the quantitative determination models obtained good performance with the determination coefficients (R2) of 0.949, 0.893, 0.968, 0.931 and 0.917 for epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epigallocatechin (EGC), epicatechin (EC) and caffeine (CAF), respectively. Such high detection accuracy shows that the spectral detection model has a strong applicability for both varieties and leaf positions. The overall results of the study revealed the potential use of Vis-NIR spectroscopy as a rapid, simple and non-destructive method for the determination of four main catechins and caffeine in fresh tea leaves, and it will play a great role in the real-time detection of tea physiological information in tea garden. [ABSTRACT FROM AUTHOR]

• An in-situ , rapid quantitative method of carotenoids in tea leaves was established. • The amount of carotenoid in the same position of leaves have no obviously difference. • The average carotenoid signal increased in tea leaves with the higher grade of maturity. • The amount of carotenoids was a good indicator of the tenderness of fresh tea leaves. The tenderness of the fresh tea leaves can affect the quality of tea products. It is important to develop a mechanized, accurate way to evaluate the quality of fresh leaves that avoids the uncertainty of a subjective evaluation. Herein, an in-situ , ultra-rapid Raman microscopy strategy to quantify carotenoids in tea leaves was established. The Raman microscopy of carotenoids distribution in leaves from new branches of 22 representative tea varieties showed that the average carotenoid signals increased from a low level in the bud to a high level in the fourth leaf, which represent different developmental stages. The concentration of carotenoids in the bud to fourth leaf, which were from 69.1 ng mg−1 to 199.5 ng mg−1, respectively. These results demonstrate that Raman imaging can serve as an in-situ , non-destructive and ultra-rapid technology for determining the tenderness of fresh tea leaves and be used in quality control for tea processing. [ABSTRACT FROM AUTHOR]