AIMC Topic: Tea

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Association between plain water intake and risk of hypertension: longitudinal analyses from the China Health and Nutrition Survey.

Frontiers in public health Jan 9, 2024
OBJECTIVE: This study aimed to investigate the prospective association between plain water intake and the risk of hypertension based on a longitudinal cohort study in China.
Nutrition Surveys Hypertension Water Drinking China Female Middle Aged Adult Humans Male Longitudinal Studies Tea
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Classification of oolong tea varieties based on computer vision and convolutional neural networks.

Journal of the science of food and agriculture Oct 30, 2023
BACKGROUND: In the contemporary food industry, accurate and rapid differentiation of oolong tea varieties holds paramount importance for traceability and quality control. However, achieving this remains a formidable challenge. This study addresses th...
Neural Networks, Computer Support Vector Machine Tea Algorithms Machine Learning
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Deep learning and targeted metabolomics-based monitoring of chewing insects in tea plants and screening defense compounds.

Plant, cell & environment Oct 26, 2023
Tea is an important cash crop that is often consumed by chewing pests, resulting in reduced yields and economic losses. It is important to establish a method to quickly identify the degree of damage to tea plants caused by leaf-eating insects and scr...
Lepidoptera Deep Learning Insecta Animals Tea Camellia sinensis
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Au-Ag OHCs-based SERS sensor coupled with deep learning CNN algorithm to quantify thiram and pymetrozine in tea.

Food chemistry Jul 4, 2023
Pesticide residue detection in food has become increasingly important. Herein, surface-enhanced Raman scattering (SERS) coupled with an intelligent algorithm was developed for the rapid and sensitive detection of pesticide residues in tea. By employi...
Neural Networks, Computer Algorithms Tea Gold Spectrum Analysis, Raman Deep Learning Pesticide Residues Metal Nanoparticles Thiram
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Tea leaf disease detection and identification based on YOLOv7 (YOLO-T).

Scientific reports Apr 13, 2023
A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an arti...
Bangladesh Artificial Intelligence Tea Plant Leaves
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Comparison of machine learning and deep learning models for evaluating suitable areas for premium teas in Yunnan, China.

PloS one Feb 24, 2023
BACKGROUND: Tea is an important economic crop in Yunnan, and the market price of premium teas such as Lao Banzhang is significantly higher than ordinary teas. For planting lands to promote, the tea industry to develop and minority lands' economies to...
China Soil Deep Learning Tea Camellia sinensis
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Mapping Pu'er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM).

PloS one Feb 7, 2023
Tea is the most popular drink worldwide, and China is the largest producer of tea. Therefore, tea is an important commercial crop in China, playing a significant role in domestic and foreign markets. It is necessary to make accurate and timely maps o...
Support Vector Machine China Tea
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Intelligent analysis of carbendazim in agricultural products based on a ZSHPC/MWCNT/SPE portable nanosensor combined with machine learning methods.

Analytical methods : advancing methods and applications Feb 2, 2023
A nano-ZnS-decorated hierarchically porous carbon (ZSHPC) was mixed with MWCNTs to obtain ZSHPC/MWCNT nanocomposites. Then, ZSHPC/MWCNTs were used to modify a screen-printed electrode, and a portable electrochemical detection system combined with mac...
Tea Porosity Carbon Machine Learning
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Grey Blight Disease Detection on Tea Leaves Using Improved Deep Convolutional Neural Network.

Computational intelligence and neuroscience Jan 17, 2023
We proposed a novel deep convolutional neural network (DCNN) using inverted residuals and linear bottleneck layers for diagnosing grey blight disease on tea leaves. The proposed DCNN consists of three bottleneck blocks, two pairs of convolutional (Co...
Tea Neural Networks, Computer Plant Diseases
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Classification of Tea Leaves Based on Fluorescence Imaging and Convolutional Neural Networks.

Sensors (Basel, Switzerland) Oct 13, 2022
The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditio...
Neural Networks, Computer Optical Imaging Tea
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