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In this study, based on the picture collection of tea shoot growth in the field, we used deep learning target detection algorithm YOLOv5 to construct a model for identifying different growth stages of tea shoots, and the testing results indicate that the model had high accuracy. Furthermore, the Image-J software and the image processing methods of threshold cutting based on Gray, RGB and HSV values were applied to process tea leaf area, and the accuracy and efficiency of different methods were compared. The results show that the accuracy of HSV-based algorithm system of cutting tea leaves and automatically calculating tea leaf area was over 94%, which had better performance than RGB-based algorithm system. The research results provide technical support for the intelligent recognition model of tea growth state and information extraction algorithm of leaf traits, and also build a theoretical basis for the development of tea bud automatic recognition module of tea plucking machinery. [ABSTRACT FROM AUTHOR]

Tea is the most popular beverage consumed in the world after water. In the present study, evaluation of inherent sugars viz. sucrose, glucose, and fructose in fresh tea leaves and in black tea were quantified using High Performance Liquid Chromatography. Fructose, glucose, and sucrose concentrations in the third and fourth tea leaves were significantly higher (P ≤0.05) than that of the bud, first, and second leaves. The significantly lowest (P ≤0.05) concentrations of glucose, sucrose, and fructose were observed in black tea manufactured using the bud and the first two leaves. There was no significant effect of time of plucking on glucose, sucrose, and fructose concentrations in fresh leaves and in black tea. There were significant (P ≤ 0.05) changes in the concentrations of sucrose, glucose, and fructose that took place during the black tea manufacturing process. As sucrose and glucose have been allegedly used for the purpose of altering the appearance and heaviness of green and black teas, the results of this study would provide useful information for the tea industry in Sri Lanka and other countries to detect the adulterations of black and green teas with sucrose and glucose.

To recognize the tender shoots for high-quality tea and to determine the picking points accurately and quickly, this paper proposes a method of recognizing the picking points of the tender tea shoots with the improved YOLO-v3 deep convolutional neural network algorithm. This method realizes the end-to-end target detection and the recognition of different postures of high-quality tea shoots, considering both efficiency and accuracy. At first, in order to predict the category and position of tender tea shoots, an image pyramid structure is used to obtain the characteristic map of tea shoots at different scales. The residual network block structure is added to the downsampling part, and the fully connected part is replaced by a 1 × 1 convolution operation at the end, ensuring accurate identification of the result and simplifying the network structure. The K-means method is used to cluster the dimension of the target box. Finally, the image data set of picking points for high-quality tea shoots is built. The accuracy of the trained model under the verification set is over 90%, which is much higher than the detection accuracy of the research methods.

The tea mosquito bugs, Helopeltis theivora Waterhouse prefer to deposit eggs mostly (66%) in the pluckable part of tea plants ( Camellia sinensis) in North East India. But, in course of control measures while they were exposed to pesticides they were apt to change the ovipositing sites on the tea shoot. In the laboratory, following exposure to endosulfan 30 EC and deltamethrin 2.8 EC H. theivora deposited around 60% eggs in the non-pluckable part in contrast to only 34% in control i.e. when untreated. [ABSTRACT FROM AUTHOR]