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[前沿资讯 ] Jayshree Teas Are Recognized as Among the Best in the World 进入全文
World Tea News 网站
“The Leafies,” which have been awarded since 2020 by UK National Tea Academy (UKTA) in collaboration with storied retailer Fortnum & Mason, are designed to “promote integrity, excellence and collaboration within the international tea industry.”This year, four hundred entries were submitted. From these, the third largest tea producer in the world, Jayshree Tea & Industries Limited with head offices in Kolkata, India, distinguished itself from the field by winning five esteemed prizes. The prestigious awards ceremony was held on November 6th at the Mayfair Hotel in London. Jayshree’s performance topped all tea producers as it earned the highest number of awards, and with it, the highest of accolades. This year, Jayshree won the gold medal in the Darjeeling category for its Balasun garden Mystic Second Flush tea, and its Turzum Garden Panned and Steamed Green Tea, which earned gold in the Experimental category. Jayshree teas are to taste as natural as possible, allowing the intrinsic flavor of the plant to dominate the brew. Jayshree Darjeeling teas from the last two years have been exemplary in aroma, body, complexity (melange of flavor notes), and aftertaste. Certain elements are favored, depending on the harvest and garden. The common characteristic that can be enjoyed across these teas is that they are smooth, even silky, particularly those plucked during early harvests.
[统计数据 ] Tea Gross Production Value (constant 2014-2016 thousand I$) from all over World Countries/Regions in 2021（FAOSTAT） 进入全文
FAO 网站
根据FAOSTAT，统计了2021年度全球47个国家/地区的茶叶总产值（单位：1000 Int. $），详细数据见表 Tea Gross Production Value (constant 2014-2016 thousand I$) from all over World Countries/Regions in 2021（FAOSTAT）。
[学术文献 ] United States tea: A synopsis of ongoing tea research and solutions to United States tea production issues 进入全文
Frontiers in Plant Science 期刊
Tea is a steeped beverage made from the leaves of Camellia sinensis. Globally, this healthy, caffeine-containing drink is one of the most widely consumed beverages. At least 50 countries produce tea and most of the production information and tea research is derived from international sources. Here, we discuss information related to tea production, genetics, and chemistry as well as production issues that affect or are likely to affect emerging tea production and research in the United States. With this review, we relay current knowledge on tea production, threats to tea production, and solutions to production problems to inform this emerging market in the United States.
[学术文献 ] Sucking pest management in tea (Camellia sinensis (L.) Kuntze) cultivation: Integrating conventional methods with bio-control strategies 进入全文
Crop Protection 期刊
Worldwide sucking pests predominantly impact the cultivation of tea (Camellia sinensis (L.) Kuntze), an economically significant crop. Sucking pests consume the plant sap by puncturing the vascular tissue of the host plant. Their trophic activity results in the curling of the tea leaves, followed by a dark brown or silver appearance with black spots. Sometimes, the symptom is confined to dry, dark, and dead leaves. All these symptoms have significantly reduced tea production over the past few decades. Sucking pests like mosquito bugs, thrips, jassids, aphids, and mites are primarily responsible for these damages in tea plants. It is crucial to use all the available eco-friendly resources to effectively implement integrated pest management strategies that reduce the sucking pest population in the tea plant ecosystem and produce pesticide-free tea. This review gives a comprehensive idea of sucking pests in tea plantations, their habits, age-old traditional methods used to control such pests, widely used synthetic pesticide treatments for instant pest control, pesticide tolerance management, and lastly, emerging sustainable methods to minimize the level of pesticide residue in this foliage crop. All these practices will help to decrease the adversities caused by pesticides to the environment.
[学术文献 ] The Circadian Clock Gene PHYTOCLOCK1 Mediates the Diurnal Emission of the Anti-Insect Volatile Benzyl Nitrile from Damaged Tea (Camellia sinensis) Plants 进入全文
Journal of Agricultural and Food Chemistry 期刊
Benzyl nitrile from tea plants attacked by various pests displays a diurnal pattern, which may be closely regulated by the endogenous circadian clock. However, the molecular mechanism by the circadian clock of tea plants that regulates the biosynthesis and release of volatiles remains unclear. In this study, the circadian clock gene CsPCL1 can activate both the expression of the benzyl nitrile biosynthesis-related gene CsCYP79 and the jasmonic acid signaling-related transcription factor CsMYC2 involved in upregulating CsCYP79 gene, thereby resulting in the accumulation and release of benzyl nitrile. Therefore, the anti-insect function of benzyl nitrile was explored in the laboratory. The application of slow-release beads of benzyl nitrile in tea plantations significantly reduced the number of tea geometrids and had positive effects on the yield of fresh tea leaves. These findings reveal the potential utility of herbivore-induced plant volatiles for the green control of pests in tea plantations.
[学术文献 ] Res4net-CBAM: a deep cnn with convolution block attention module for tea leaf disease diagnosis 进入全文
Multimedia Tools and Applications 期刊
In this study, we propose Res4net-CBAM, a deep convolutional neural network (CNN) specifically designed for tea leaf disease diagnosis, aiming to reduce the model’s complexity and improve disease identification accuracy. The Res4net-CBAM model utilizes a residual block-based Res4net architecture with a network interactive convolutional block attention module (CBAM) to accurately extract complex features associated with different diseases. We conducted extensive experiments to compare the performance of our model with standard CNN models such as AlexNet, VGG16, ResNet50, DenseNet121, and InceptionV3, based on metrics such as accuracy, precision, recall, and F1-score. Our results demonstrate that the Res4net-CBAM model outperforms all other models, achieving an average recognition accuracy of 98.27% on self-acquired tea leaf disease data samples. Specifically, the Res4net-CBAM model achieved an average sensitivity of 98.39%, specificity of 98.26%, precision of 98.35%, and F1-score of 98.37%, while utilizing the Adagrad optimizer with a learning rate of 0.001. Moreover, our model surpasses some recent and existing works in this field, highlighting its effectiveness in diagnosing tea leaf diseases.
