AIMC Topic: Camellia sinensis
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Estimating Tea Plant Physiological Parameters Using Unmanned Aerial Vehicle Imagery and Machine Learning Algorithms.
Sensors (Basel, Switzerland)
Mar 21, 2025
Tea ( L.) holds agricultural economic value and forestry carbon sequestration potential, with Taiwan's annual tea production exceeding TWD 7 billion. However, climate change-induced stressors threaten tea plant growth, photosynthesis, yield, and qual...
Machine learning unveils the role of biochar application in enhancing tea yield by mitigating soil acidification in tea plantations.
The Science of the total environment
Jan 29, 2025
Biochar, a widely utilized soil amendment in environmental applications, has been employed to enhance tea cultivation. This study utilized three machine learning models to investigate the effects of biochar on tea growth and yield, with the random fo...
Determination of quality differences and origin tracing of green tea from different latitudes based on TG-FTIR and machine learning.
Food research international (Ottawa, Ont.)
Jan 27, 2025
Latitude differences can significantly affect the quality of tea, while in-depth research in this field is lacking. This study investigates green teas from different latitudes in China using thermogravimetric analysis coupled with infrared spectrosco...
Tea grading, blending, and matching based on computer vision and deep learning.
Journal of the science of food and agriculture
Dec 22, 2024
BACKGROUND: Accurate tea blending assessment and sample matching are critical in the tea production process. Traditional methods face efficiency and accuracy challenges, which can be addressed by advances in computer vision and deep learning. This st...
Antimicrobial Activity of Tea and Agarwood Leaf Extracts Against Multidrug-Resistant Microbes.
BioMed research international
Dec 19, 2024
Emerging multidrug-resistant (MDR) strains are the main challenges to the progression of new drug discovery. To diminish infectious disease-causing pathogens, new antibiotics are required while the drying pipeline of potent antibiotics is adding to t...
Promoting LC-QToF based non-targeted fingerprinting and biomarker selection with machine learning for the discrimination of black tea geographical origin.
Food chemistry
Nov 23, 2024
Traceability and mislabelling of black tea for their geographical origin is known as a major fraud concern of the sector. Discrimination among various geographical indications (GIs) can be challenging due to the complexity of chemical fingerprints in...
Development of analytical "aroma wheels" for Oolong tea infusions (Shuixian and Rougui) and prediction of dynamic aroma release and colour changes during "Chinese tea ceremony" with machine learning.
Food chemistry
Oct 4, 2024
The flavour of tea as a worldwide popular beverage has been studied extensively. This study aimed to apply established flavour analysis techniques (GC-MS, GC-O-MS and APCI-MS/MS) in innovative ways to characterise the flavour profile of oolong tea in...
Intelligent identification of picking periods of Lu'an Guapian tea by an indicator displacement colorimetric sensor array combined with machine learning.
Food research international (Ottawa, Ont.)
Aug 22, 2024
Lu'an Gua Pian (LAGP) tea is one of the most famous green teas in China. The quality of green tea is related to its picking periods, especially the green tea before Qingming Festival (usually April 6th) is highly praised as precious in the market. In...
Lightweight CNN combined with knowledge distillation for the accurate determination of black tea fermentation degree.
Food research international (Ottawa, Ont.)
Aug 18, 2024
Black tea is the second most common type of tea in China. Fermentation is one of the most critical processes in its production, and it affects the quality of the finished product, whether it is insufficient or excessive. At present, the determination...
Olfactory analysis of oolong tea sensory quality using composite nano-colorimetric sensor array.
Food research international (Ottawa, Ont.)
Aug 11, 2024
Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile ...
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