Special Issue "Advances in Tea Tree Research"

A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: 31 October 2022 | Viewed by 725

Special Issue Editors

Dr. Lanting Zeng
E-Mail Website
Guest Editor
Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement & Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, No. 723 Xingke Road, Tianhe District, Guangzhou 510650, China
Interests: tea; Camellia sinensis; quality; secondary metabolite; biosynthesis; stress response; biological function
Dr. Yinyin Liao
E-Mail Website
Guest Editor
Guangdong Provincial Key Laboratory of Applied Botany & Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, South China Botanical Garden, Chinese Academy of Sciences, No. 723 Xingke Road, Tianhe District, Guangzhou 510650, China
Interests: tea; Camellia sinensis; secondary metabolite; insect; stress response; biological function

Special Issue Information

Dear Colleagues,

Tea tree (Camellia sinensis) is used to produce the second most popular beverage worldwide after water. To date, great progress has been made in the study of tea tree. Whole-genome sequencing has laid a foundation for the cloning of important functional genes and marker-assisted selection in tea breeding. A number of special tea resources have been collected and identified, providing the resource base for the breeding of special cultivars and the development of diversified products. The metabolic pathways and regulation mechanisms of several secondary metabolites have been clarified, promoting the elucidation of the formation mechanism of economic characters of tea tree. The research on green production technology for tea tree has helped to ensure the quality and safety of tea. This Special Issue of Plants will highlight omics, resources and breeding, secondary metabolism, and green production technology during tea tree growth.

Dr. Lanting Zeng
Dr. Yinyin Liao
Guest Editors

Manuscript Submission Information

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Keywords

  • Camellia sinensis
  • omics
  • biotic stress
  • abiotic stress
  • stress resistance
  • breeding
  • development
  • secondary metabolites

Published Papers (1 paper)

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Research

Article
Research on Tea Tree Growth Monitoring Model Using Soil Information
Plants 2022, 11(3), 262; https://doi.org/10.3390/plants11030262 - 19 Jan 2022
Viewed by 481
Abstract
Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of [...] Read more.
Crop growth monitoring is an important component of agricultural information, and suitable soil temperature (ST), soil moisture content (SMC) and soil electrical conductivity (SEC) play a key role in crop growth. Real-time monitoring of the three soil parameters to predict the growth of tea plantation helps tea trees grow healthily and to accurately grasp the growth trend of tea trees. In this paper, five different models based on the polynomial model and power model were used to construct the soil temperature, soil water content and soil conductivity and tea plantation growth monitoring models. Experiments proved that tea plantation growth were positively correlated with ST and negatively correlated with SMC and SEC, and among the constructed models, the ternary cubic polynomial model was the best, and R square (R2) of the constructed models were 0.6369, 0.4510 and 0.5784, respectively, indicating that SEC was the most relevant to tea plantation growth maximum. To improve the prediction accuracy, a model based on sum of soil temperature (SST), sum of soil water content (SSMC) and sum of soil conductivity (SSEC) was proposed, and the experiments also showed that the ternary cubic polynomial model was the best, with 0.9638, 0.9733 and 0.9660, respectively. At the same time, a model incorporating three parameters such as soil temperature, soil water content and soil conductivity was also suggested, with 0.6605 and 0.9761, respectively, which effectively improved the prediction accuracy. Validation experiments were conducted. Twelve data sets were utilized to verify the performance of the model. The experiments showed that the regressions in the polynomial models achieved a better prediction effect. Finally, a long short-term memory (LSTM) network prediction model optimized by the bald eagle search algorithm (BES) was also constructed, and R2, root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of prediction were 0.8666, 0.0629, 0.0040, 0.0436 and 10.5257, respectively, which significantly outperformed the LSTM network and achieved better performance. The model proposed in this paper can be used to predict the actual situation during the growing period of tea leaves, which can improve the production management of tea plantations and also provide a scientific basis for accurate tea planting and a decision basis for agricultural policy formulation, as well as provide technical support for the realization of agricultural modernization. Full article
(This article belongs to the Special Issue Advances in Tea Tree Research)
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