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Related Experiment Videos

Adaptive classification and grading model of cigar wrapper leaf based on improved ResNet algorithm.

Chaofan Du1, Ruiqi Wang2, Tianyi Wu2

  • 1LongyanTobacco Company, Longyan, 364000, China.

Scientific Reports
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an automated cigar wrapper leaf grading system using deep learning. The AI model achieves high accuracy, improving efficiency and standardization in cigar production.

Keywords:
Cigar gradingDeep learningImage classificationModel construction

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Machine Learning

Background:

  • Cigar wrapper leaf grading is crucial for quality but relies on inefficient manual sorting.
  • Current manual methods suffer from low efficiency and inconsistent grading standards.
  • A need exists for an intelligent, automated system to improve cigar wrapper leaf classification.

Purpose of the Study:

  • To develop and validate a deep learning framework for automated cigar wrapper leaf grading.
  • To enhance feature extraction using a Mask-augmented Fourth Channel (Mask4Ch) module with segmentation masks.
  • To improve classification accuracy and standardization in the cigar industry.

Main Methods:

  • A ResNet-50 backbone integrated with a Mask4Ch module was used for feature extraction.
  • A dual-head training strategy combined Cross-Entropy (CE) and Cumulative Ordinal Regression (CORAL).
  • Optimizations included weighted random sampling (WRS) and exponential moving average (EMA) for improved generalization.

Main Results:

  • The model achieved 94.39% accuracy, 0.950 macro-F1 score, and 0.964 weighted Kappa (QWK).
  • Mean Average Precision (mAP) reached 0.985 on the test dataset.
  • The results demonstrate the effectiveness of deep learning for automated cigar wrapper grading.

Conclusions:

  • Deep learning offers a powerful solution for automated cigar wrapper leaf grading, surpassing manual limitations.
  • The developed framework provides a technical foundation for optimizing grading systems and potential mobile deployment.
  • This research advances automation and standardization within the cigar production industry.