Advances of Vis/NIRS and imaging techniques assisted by AI for tea processing

Area of Science:

• Agricultural Science
• Food Science
• Analytical Chemistry
• Computer Science

Background:

• Tea quality is crucial, traditionally assessed via subjective human sensory evaluation.
• Conventional methods are prone to subjectivity and environmental variability.
• Objective, rapid, and non-destructive techniques are needed for modern tea processing.

Purpose of the Study:

• To review recent advancements (2019-2025) in applying AI-assisted Vis/NIRS and HSI for tea quality monitoring.
• To explore the integration of spectral techniques with artificial intelligence in tea production.
• To identify challenges and future trends for practical implementation in the tea industry.

Main Methods:

• Review of scientific literature on Vis/NIRS and HSI applications in tea quality assessment.
• Analysis of AI algorithms used for spectral data processing and decision-making.
• Focus on techniques integrated into tea production processes.

Main Results:

• Vis/NIRS and HSI, when augmented by AI, provide effective, non-destructive tea quality monitoring.
• These integrated technologies enable rapid spectral analysis and automated decision-making.
• Significant progress has been made in applying these methods from 2019 to 2025.

Conclusions:

• AI-assisted Vis/NIRS and HSI represent a significant technological leap for objective tea quality control.
• These methods offer a promising alternative to traditional sensory evaluations in automated tea processing.
• Further research is needed to address challenges for widespread adoption in the tea industry.