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Computer vision-based method for classification of wheat grains using artificial neural network.

Kadir Sabanci1, Ahmet Kayabasi1, Abdurrahim Toktas1

  • 1Department of Electrical and Electronics Engineering, Engineering Faculty, Karamanoglu Mehmetbey University, 70100, Karaman, Turkey.

Journal of the Science of Food and Agriculture
|October 9, 2016
PubMed
Summary

A computer vision system using artificial neural networks (ANN) accurately classifies wheat grains as bread or durum. This simplified ANN model achieved high accuracy, demonstrating potential for automated grain classification.

Keywords:
artificial neural network (ANN)classificationimage processingmultilayer perceptronwheat grains

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Automated classification of wheat grains is crucial for agricultural quality control.
  • Distinguishing between bread and durum wheat varieties traditionally requires manual inspection.
  • Developing efficient and accurate automated methods is essential for the grain industry.

Purpose of the Study:

  • To present a simplified computer vision-based application for classifying wheat grains.
  • To utilize artificial neural networks (ANN), specifically multilayer perceptron (MLP), for accurate classification.
  • To evaluate the effectiveness of visual features in differentiating bread and durum wheat.

Main Methods:

  • Acquired images of 100 bread and 100 durum wheat grains using a high-resolution camera.
  • Extracted visual features including dimensions, colors, and textures using image-processing techniques (IPTs).
  • Trained and tested an ANN model with 200 wheat grains, utilizing 21 diversified visual features.

Main Results:

  • Identified seven key input parameters using the CfsSubsetEval algorithm to simplify the ANN model.
  • The simplified ANN model achieved a high classification accuracy.
  • The best performance was recorded with a mean absolute error (MAE) of 9.8 × 10-6.

Conclusions:

  • The proposed computer vision-based classifier can successfully automate the classification of wheat grains.
  • This technology offers a reliable method for distinguishing between bread and durum wheat varieties.
  • The study highlights the potential of ANN and IPTs for broader applications in grain analysis.