Multidisciplinary Advances in Tea Science: Smart Cultivation, Digital Processing, and Health Innovation

Topic Information

Dear Colleagues,

Tea, one of the world's most popular beverages, is a globally significant crop and cultural icon. Its research spans agriculture, food science, environmental sustainability, health sciences, and economics. Emerging multidisciplinary approaches—including spectroscopy, phenomics, AI, intelligent sensing, big data, and molecular biology—are revolutionizing tea science, offering novel solutions to enhance production efficiency, quality control, and sustainable practices, while fostering health and economic value.

This Topic focuses on advancing multidisciplinary methodologies across the tea industry chain, from cultivation and processing to product development. Key themes include (1) multi-modal data integration (remote sensing, imaging, and AI) for precision agriculture in tea growth and ecosystem management; (2) spectral/thermal analysis and intelligent sensory systems (e.g., e-nose and e-tongue) for smart processing technologies and digitized quality assessment; (3) bioactive compounds in tea/flower tea and their functional mechanisms; and (4) advanced detection methods using biochemical, optoelectronic, and novel materials for tea quality and safety. By bridging multidisciplinary methods with industry needs, this Topic aims to catalyze the modernization, sustainability, and global competitiveness of tea production systems.

Dr. Chunwang Dong
Dr. Lin Chen
Dr. Yang Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agronomy agronomy	3.4	6.7	2011	17.2 Days	CHF 2600	Submit
Crops crops	1.9	2.4	2021	23.5 Days	CHF 1200	Submit
Foods foods	5.1	8.7	2012	14.9 Days	CHF 2900	Submit
Plants plants	4.1	7.6	2012	17.7 Days	CHF 2700	Submit
Agriculture agriculture	3.6	6.3	2011	18 Days	CHF 2600	Submit
Horticulturae horticulturae	3.0	5.1	2015	17.1 Days	CHF 2200	Submit

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Published Papers (3 papers)

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All Agronomy Crops Foods Plants Agriculture Horticulturae

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21 pages, 4871 KB  

Open AccessArticle

Assessment of Tenderness and Anthocyanin Content in Zijuan Tea Fresh Leaves Using Near-Infrared Spectroscopy Fused with Visual Features

by Shuya Chen, Fushuang Dai, Mengqi Guo and Chunwang Dong

Foods 2025, 14(17), 2938; https://doi.org/10.3390/foods14172938 - 22 Aug 2025

Viewed by 372

Abstract

Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for [...] Read more.

Focusing on the characteristic tea resource Zijuan tea, this study addresses the difficulty of grading on production lines and the complexity of quality evaluation. On the basis of the fusion of near-infrared (NIR) spectroscopy and visual features, a novel method is proposed for classifying different tenderness levels and quantitatively assessing key anthocyanin components in Zijuan tea fresh leaves. First, NIR spectra and visual feature data were collected, and anthocyanin components were quantitatively analyzed using UHPLC-Q-Exactive/MS. Then, four preprocessing techniques and three wavelength selection methods were applied to both individual and fused datasets. Tenderness classification models were developed using Particle Swarm Optimization–Support Vector Machine (PSO-SVM), Random Forest (RF), and Convolutional Neural Networks (CNNs). Additionally, prediction models for key anthocyanin content were established using linear Partial Least Squares Regression (PLSR), nonlinear Support Vector Regression (SVR) and RF. The results revealed significant differences in NIR spectral characteristics across different tenderness levels. Model combinations such as TEX + Medfilt + RF and NIR + Medfilt + CNN achieved 100% accuracy in both training and testing sets, demonstrating robust classification performance. The optimal models for predicting key anthocyanin contents also exhibited excellent predictive accuracy, enabling the rapid and nondestructive detection of six major anthocyanin components. This study provides a reliable and efficient method for intelligent tenderness classification and the rapid, nondestructive detection of key anthocyanin compounds in Zijuan tea, holding promising potential for quality control and raw material grading in the specialty tea industry. Full article

(This article belongs to the Topic Multidisciplinary Advances in Tea Science: Smart Cultivation, Digital Processing, and Health Innovation)

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17 pages, 16105 KB  

Open AccessArticle

ITD-YOLO: An Improved YOLO Model for Impurities in Premium Green Tea Detection

by Zezhong Ding, Yanfang Li, Bin Hu, Zhiwei Chen, Houzhen Jia, Yali Shi, Xingmin Zhang, Xuesong Zhu, Wenjie Feng and Chunwang Dong

Foods 2025, 14(9), 1554; https://doi.org/10.3390/foods14091554 - 28 Apr 2025

Cited by 1 | Viewed by 601

Abstract

During the harvesting and preparation of tea, it is common for tea to become mixed with some impurities. Eliminating these impurities is essential to improve the quality of famous green tea. At present, this sorting procedure heavily depends on manual efforts, which include [...] Read more.

During the harvesting and preparation of tea, it is common for tea to become mixed with some impurities. Eliminating these impurities is essential to improve the quality of famous green tea. At present, this sorting procedure heavily depends on manual efforts, which include high labor intensity, low sorting efficiency, and high sorting costs. In addition, the hardware performance is poor in actual production, and the model is not suitable for deployment. To solve this technical problem in the industry, this article proposes a lightweight algorithm for detecting and sorting impurities in premium green tea in order to improve sorting efficiency and reduce labor intensity. A custom dataset containing four categories of impurities was created. This dataset was employed to evaluate various YOLOv8 models, ultimately leading to the selection of YOLOv8n as the base model. Initially, four loss functions were compared in the experiment, and Focaler_mpdiou was chosen as the final loss function. Subsequently, this loss function was applied to other YOLOv8 models, leading to the selection of YOLOv8m-Focaler_mpdiou as the teacher model. The model was then pruned to achieve a lightweight model at the expense of detection accuracy. Finally, knowledge distillation was applied to enhance its detection performance. Compared to the base model, it showed advancements in P, R, mAP, and FPS by margins of 0.0051, 0.0120, and 0.0094 and an increase of 72.2 FPS, respectively. Simultaneously, it achieved a reduction in computational complexity with GFLOPs decreasing by 2.3 and parameters shrinking by 860350 B. Afterwards, we further demonstrated the model’s generalization ability in black tea samples. This research contributes to the technological foundation for sophisticated impurity classification in tea. Full article

(This article belongs to the Topic Multidisciplinary Advances in Tea Science: Smart Cultivation, Digital Processing, and Health Innovation)

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16 pages, 18260 KB  

Open AccessArticle

Improvement of Summer Green Tea Quality Through an Integrated Shaking and Piling Process

by Zheng Tu, Sixu Li, Anan Xu, Qinyan Yu, Yanyan Cao, Meng Tao, Shanshan Wang and Zhengquan Liu

Foods 2025, 14(7), 1284; https://doi.org/10.3390/foods14071284 - 7 Apr 2025

Viewed by 728

Abstract

Summer green tea often suffers from an inferior flavor, attributed to its bitterness and astringency. In this study, an integrated shaking and piling process was performed to improve the flavor of summer green tea. The results demonstrated a significant improvement in the sweet [...] Read more.

Summer green tea often suffers from an inferior flavor, attributed to its bitterness and astringency. In this study, an integrated shaking and piling process was performed to improve the flavor of summer green tea. The results demonstrated a significant improvement in the sweet and kokumi flavors, accompanied by a reduction in umami, astringency, and bitterness following the treatment. Additionally, the yellowness and color saturation were also enhanced by the treatment. A total of 146 non-volatile metabolites (NVMs) were identified during the study. The elevated levels of sweet-tasting amino acids (L-proline, L-glutamine, and L-threonine), soluble sugars, and peptides (such as gamma-Glu-Gln and glutathione) contributed to the enhanced sweetness and kokumi. Conversely, the decreased levels of ester-catechins, flavonoid glycosides, and procyanidins resulted in a reduction in umami, astringency, and bitterness. Furthermore, the decreased levels of certain NVMs, particularly ascorbic acid and saponarin, played a crucial role in enhancing the yellowness and color saturation of the summer green tea. Our findings offered a novel theoretical framework and practical guidelines for producing high-quality summer green tea. Full article

(This article belongs to the Topic Multidisciplinary Advances in Tea Science: Smart Cultivation, Digital Processing, and Health Innovation)

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