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Predicting the Degree of Fresh Tea Leaves Withering Using Image Classification Confidence.

Mengjie Wang1,2, Yali Shi1, Yaping Li2

  • 1Tea Research Institute of Shandong Academy of Agricultural Sciences, Jinan 250100, China.

Foods (Basel, Switzerland)
|April 16, 2025
PubMed
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This study introduces a novel model for detecting the withering degree of fresh tea leaves using image classification confidence. The method accurately assesses moisture content, crucial for high-quality tea production.

Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Food Science

Background:

  • Ensuring fresh tea leaf quality requires rapid, non-destructive methods to assess wilting.
  • Current methods may lack the precision needed for real-time processing adjustments.

Purpose of the Study:

  • To develop an accurate and efficient model for detecting the withering degree of fresh tea leaves.
  • To establish a reliable method for calculating moisture percentage and determining wilting levels.

Main Methods:

  • A fresh tea withering degree detection model based on image classification confidence.
  • Incorporation of Receptive-Field Attention Convolution (RFAConv) and Cross-Stage Feature Fusion Coordinate Attention (C2f_CA) modules.
  • A weighted method combining confidence levels and moisture labels to calculate moisture percentage.
Keywords:
YOLOv8confidence weightingdegree of witheringfresh tea leavesmoisture content

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Main Results:

  • The proposed model achieved a classification accuracy of 92.7%, improving detection accuracy by 0.156.
  • Excellent predictive performance for moisture content with Rp=0.9983, RMSEP=0.006278, and RPD=39.2513.
  • Outperformed traditional Partial Least Squares (PLS) and Convolutional Neural Network (CNN) methods.

Conclusions:

  • The developed model provides accurate and rapid detection of tea leaf withering.
  • Offers crucial technical support for online determination during tea processing.
  • Enhances quality control in tea production through precise wilting assessment.