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Grain classifier with computer vision using adaptive neuro-fuzzy inference system.

Kadir Sabanci1, Abdurrahim Toktas1, Ahmet Kayabasi1

  • 1Department of Electrical and Electronics Engineering, Engineering Faculty, Karamanoglu Mehmetbey University, Karaman, Turkey.

Journal of the Science of Food and Agriculture
|February 15, 2017
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Summary

A new computer vision system accurately classifies wheat grains as bread or durum using an adaptive neuro-fuzzy inference system (ANFIS). This automated system achieves high accuracy, simplifying industrial applications.

Keywords:
adaptive neuro-fuzzy inference system (ANFIS)classificationfeature selectionimage processingwheat grains

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Area of Science:

  • Agricultural technology
  • Computer vision
  • Machine learning

Background:

  • Wheat grain classification is crucial for agricultural industries.
  • Distinguishing between bread and durum wheat varieties is essential.
  • Existing methods may lack efficiency or accuracy in automated classification.

Purpose of the Study:

  • To design and evaluate a computer vision-based classifier for wheat grain differentiation.
  • To utilize an adaptive neuro-fuzzy inference system (ANFIS) for enhanced classification accuracy.
  • To identify the most effective visual features for simplifying the classification process.

Main Methods:

  • Acquired high-resolution images of 200 wheat grains (100 bread, 100 durum).
  • Employed image processing techniques (IPTs) to extract visual features (dimension, color, texture).
  • Developed and tested an ANFIS classifier, including feature subset analysis and simplification.

Main Results:

  • A simplified ANFIS classifier with seven selected features demonstrated optimal performance.
  • The simplified classifier achieved 99.46% accuracy in computation.
  • Wheat grains were classified with 100% accuracy during the testing phase.

Conclusions:

  • A highly accurate automated system for classifying wheat grains was successfully developed.
  • The proposed ANFIS classifier offers potential for integration into industrial applications.
  • The system enables precise differentiation of wheat varieties, improving quality control.