AIMC Topic: Camellia sinensis

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Mathematical optimization of multilinear and artificial neural network regressions for mineral composition of different tea types infusions.

Scientific reports Aug 7, 2024
The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish...
Camellia sinensis Tea Minerals Neural Networks, Computer Regression Analysis
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Characterization and exploration of dynamic variation of volatile compounds in vine tea during processing by GC-IMS and HS-SPME/GC-MS combined with machine learning algorithm.

Food chemistry Jul 31, 2024
It is imperative to unravel the dynamic variation of volatile components of vine tea during processing to provide guidance for tea quality evaluation. In this study, the dynamic changes of volatile compounds of vine tea during processing were charact...
Food Handling Solid Phase Microextraction Ion Mobility Spectrometry Odorants Volatile Organic Compounds Machine Learning Algorithms Tea Gas Chromatography-Mass Spectrometry Camellia sinensis
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Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network.

Scientific reports Jun 18, 2024
Globally, tea production and its quality fundamentally depend on tea leaves, which are susceptible to invasion by pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element in preventing and controlling th...
Neural Networks, Computer Image Processing, Computer-Assisted Tea Plant Diseases Bangladesh Plant Leaves Camellia sinensis
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Advanced deep learning algorithm for instant discriminating of tea leave stress symptoms by smartphone-based detection.

Plant physiology and biochemistry : PPB May 22, 2024
The primary challenges in tea production under multiple stress exposures have negatively affected its global market sustainability, so introducing an infield fast technique for monitoring tea leaves' stresses has tremendous urgent needs. Therefore, t...
Plant Diseases Deep Learning Stress, Physiological Smartphone Algorithms Plant Leaves Camellia sinensis Tea
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YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea.

Sensors (Basel, Switzerland) May 1, 2024
In order to efficiently identify early tea diseases, an improved YOLOv8 lesion detection method is proposed to address the challenges posed by the complex background of tea diseases, difficulty in detecting small lesions, and low recognition rate of ...
Neural Networks, Computer Algorithms Plant Leaves Plant Diseases Camellia sinensis Tea
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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 Tea Insecta Deep Learning Camellia sinensis Animals
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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...
Camellia sinensis Tea Soil Deep Learning China
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A New Generation of ResNet Model Based on Artificial Intelligence and Few Data Driven and Its Construction in Image Recognition Model.

Computational intelligence and neuroscience Mar 19, 2022
The paper proposes an A-ResNet model to improve ResNet. The residual attention module with shortcut connection is introduced to enhance the focus on the target object; the dropout layer is introduced to prevent the overfitting phenomenon and improve ...
Artificial Intelligence Algorithms Neural Networks, Computer Computer Simulation Camellia sinensis
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Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms.

Scientific reports Oct 15, 2020
Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problemati...
Spectrum Analysis Chlorophyll Camellia sinensis Nitrogen Algorithms Plant Leaves Machine Learning
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Bio-transformation of green tea infusion with tannase and its improvement on adipocyte metabolism.

Enzyme and microbial technology Dec 23, 2019
Catechins in green tea possess various health benefits. Enzymatic treatment improves physiological activities by inducing bioconversion of catechins. Here, we investigated the effect of green tea infusion (GT) after tannase treatment, which transform...
Adipocytes Adiponectin Biocatalysis Camellia sinensis Catechin Tea Food Handling Cell Differentiation Gallic Acid Aspergillus Plant Extracts Carboxylic Ester Hydrolases NIH 3T3 Cells Plant Leaves Fungal Proteins Uncoupling Protein 1 Animals Mice
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