AIMC Topic: Tea

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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 ...
Volatile Organic Compounds Smell Odorants Taste Colorimetry Support Vector Machine Humans Camellia sinensis Tea
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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...
Odorants Volatile Organic Compounds Tea Gas Chromatography-Mass Spectrometry Ion Mobility Spectrometry Food Handling Solid Phase Microextraction Machine Learning Algorithms Camellia sinensis
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Artificial neural networks in soil quality prediction: Significance for sustainable tea cultivation.

The Science of the total environment Jul 3, 2024
In today's era artificial intelligence is quite popular, one of the most effective algorithms used is Artificial Neural Networks (ANN). In this study, the determination of soil quality using the Soil Management Assessment Framework (SMAF) model in ar...
Neural Networks, Computer Soil Tea Agriculture Environmental Monitoring
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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...
Bangladesh Tea Plant Diseases Plant Leaves Camellia sinensis Neural Networks, Computer Image Processing, Computer-Assisted
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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 Leaves Camellia sinensis Tea Plant Diseases Stress, Physiological Algorithms Deep Learning Smartphone
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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 ...
Camellia sinensis Tea Plant Diseases Neural Networks, Computer Algorithms Plant Leaves
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Small object detection algorithm incorporating swin transformer for tea buds.

PloS one Mar 21, 2024
Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identifica...
Electric Power Supplies Tea Neural Networks, Computer Algorithms Generalization, Psychological
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A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

Scientific reports Feb 20, 2024
In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML)...
Mental Recall Algorithms Plant Leaves Tea Machine Learning
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Artificial Intelligence Sensing: Effective Flavor Blueprinting of Tea Infusions for a Quality Control Perspective.

Molecules (Basel, Switzerland) Jan 23, 2024
Tea infusions are the most consumed beverages in the world after water; their pleasant yet peculiar flavor profile drives consumer choice and acceptance and becomes a fundamental benchmark for the industry. Any qualification method capable of objecti...
Quality Control Volatile Organic Compounds Tea Humans Odorants Artificial Intelligence Smell China
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