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  1. Home
  3. Classification Of Sun-dried Bulk Raisins By Using Statistical Texture Features.
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  3. Classification Of Sun-dried Bulk Raisins By Using Statistical Texture Features.

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Classification of Sun-Dried Bulk Raisins by Using Statistical Texture Features.

Mostafa Khojastehnazhand1, Amir Kazemi2

  • 1Department of Mechanical Engineering, Faculty of Engineering, University of Bonab, Bonab, Iran.

Journal of Texture Studies
|June 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study developed an expert system using machine vision to classify bulk sun-dried raisins, improving quality assessment. The system accurately identifies impurities and damaged grains, crucial for Iran's raisin exports.

Keywords:
classificationfeature selectionmachine visionraisintexture features

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Area of Science:

  • Agricultural Engineering
  • Computer Vision
  • Food Science

Background:

  • Sun-dried raisins are a key Iranian export, but visual classification is challenging due to impurities.
  • Accurate quality assessment is vital for producers and buyers in the raisin trade.

Purpose of the Study:

  • To develop an expert system for classifying bulk sun-dried raisins using machine vision.
  • To differentiate raisins from dark-colored impurities and damaged grains.

Main Methods:

  • A machine vision setup captured 525 raisin images.
  • Textural features were extracted using Gray Level Co-occurrence Matrix (GLCM), Gray-Level Run-length Matrix (GLRM), and Local Binary Pattern (LBP).
  • Classification models including Discriminant Analysis (DA) were evaluated.

Main Results:

  • The Discriminant Analysis (DA) model achieved 98.61% accuracy using all features and 97.92% using GLCM features.
  • Feature selection algorithms (MRMR, CST) were used to optimize classification.
  • High accuracies were obtained for multi-class raisin classification.

Conclusions:

  • The proposed machine vision expert system effectively classifies bulk sun-dried raisins.
  • This technology can enhance raisin purity and quality measurement systems.
  • The findings support improved quality control in the raisin export industry.