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Bread and durum wheat classification using wavelet based image fusion.

Kadir Sabanci1, Muhammet Fatih Aslan1, Akif Durdu2

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

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Accurate identification of bread and durum wheat is crucial for product quality. Machine learning classification of fused UV and white light images significantly improved wheat species identification accuracy.

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Image Processing

Background:

  • Wheat is a vital food source, with distinct bread and durum varieties essential for flour and feed.
  • Accurate differentiation between bread and durum wheat is critical for maintaining product quality.
  • Traditional identification methods are being enhanced by advanced imaging and machine learning techniques.

Purpose of the Study:

  • To develop and evaluate a novel method for distinguishing between bread and durum wheat species.
  • To compare the effectiveness of machine learning algorithms on individual and fused wheat images.
  • To assess the impact of wavelet-based image fusion on classification accuracy.

Main Methods:

  • Acquisition of ultraviolet (UV) and white light (WL) images for both bread and durum wheat.
  • Classification of wheat types using various machine learning (ML) algorithms, including Support Vector Machine (SVM) and Multilayer Perceptron (MLP).
  • Application of a wavelet-based image fusion technique to combine UV and WL images.

Main Results:

  • Individual UV and WL images achieved a maximum classification accuracy of 94.83% using SVM and MLP algorithms.
  • The fused image, utilizing wavelet-based fusion, resulted in a higher classification accuracy of 98.28%.
  • Both MLP and SVM algorithms demonstrated equal success in classifying the fused images, indicating enhanced identification capabilities.

Conclusions:

  • Wavelet-based image fusion significantly enhances the classification accuracy for identifying wheat species.
  • The fused image provides superior identification ability compared to using only UV or WL images.
  • This advanced method offers a more effective approach to distinguishing between bread and durum wheat varieties.