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Machine learning approach for wheat variety identification using single-seed imaging.

Hossein Bagherpour1, Siavash Shamohammadi2

  • 1Department of Biosystems Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran. h.bagherpour@basu.ac.ir.

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Automated wheat variety identification using deep learning achieved 92.19% accuracy. The CNN-GAP model offers stable, real-time classification for precision agriculture, outperforming traditional methods.

Keywords:
CNNDeep learningSeed classificationWheat variety

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate wheat varietal identification is crucial for agriculture but challenging due to cultivar similarity.
  • Traditional methods are subjective and labor-intensive, hindering efficiency and reliability.

Purpose of the Study:

  • To develop and compare automated wheat varietal classification frameworks.
  • To evaluate handcrafted versus deep-learning feature extraction methods.
  • To assess the stability and real-time applicability of different models.

Main Methods:

  • Image acquisition of six Iranian wheat cultivars under controlled conditions.
  • Feature extraction using Principal Component Analysis (PCA) with Multi-Layer Perceptron (MLP).
  • Deep feature learning with Convolutional Neural Networks (CNNs) using Global Average Pooling (GAP) and Fully Connected Layers (FCL) classifier heads.

Main Results:

  • The CNN-GAP model achieved the highest accuracy (92.19%) and demonstrated superior generalization stability.
  • PCA-enhanced MLP yielded 86.0% accuracy.
  • Cross-domain testing revealed sensitivity to domain shifts, necessitating species-specific data.

Conclusions:

  • The lightweight CNN-GAP architecture is suitable for real-time, low-cost deployment in precision agriculture.
  • Automated classification significantly improves upon traditional methods for wheat seed identification.
  • Further research should focus on domain adaptation for broader applicability.