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A Computer Vision System Based on Majority-Voting Ensemble Neural Network for the Automatic Classification of Three

Razieh Pourdarbani1, Sajad Sabzi1, Davood Kalantari2

  • 1Department of Biosystems Engineering, College of Agriculture, University of Mohaghegh Ardabili, Ardabil 56199-11367, Iran.

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Summary

Accurate chickpea variety identification is crucial to prevent fraud. A computer vision system using advanced machine learning achieved 99.10% accuracy in classifying Adel, Arman, and Azad chickpea varieties.

Keywords:
Cicer arietinum L.chickpeaclassificationcomputer visionfeature selectionhybrid ANNimage processinglegumemachine learningmajority votingsegmentation

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate crop cultivar identification is vital to prevent fraudulent sales and ensure specific applications.
  • Human expert classification can be subjective and inconsistent due to factors like fatigue.
  • Chickpea (Cicer arietinum L.) is a globally important legume with several visually similar varieties.

Purpose of the Study:

  • To develop and present a computer vision system for the automatic classification of three distinct chickpea varieties: Adel, Arman, and Azad.
  • To address the challenge of visually similar chickpea cultivars through automated analysis.
  • To enhance the reliability and objectivity of chickpea variety identification.

Main Methods:

  • Image segmentation using Hue Saturation Intensity (HSI) color space thresholding.
  • Extraction of color and textural features (Gray Level Co-occurrence Matrix - GLCM) from chickpea images.
  • Feature selection using a hybrid Artificial Neural Network-Cultural Algorithm (ANN-CA) to identify the five most effective discriminant features.
  • Classification using an ensemble methodology (ANN-PSO/ACO/HS majority voting - MV) combining three hybrid classifiers (ANN-PSO, ANN-ACO, ANN-HS).

Main Results:

  • The hybrid ANN-CA effectively selected five key features for classification.
  • The ensemble ANN-PSO/ACO/HS-MV classifier achieved a high average classification accuracy of 99.10 ± 0.75%.
  • The system demonstrated robust performance over 1000 random iterations on the test set.

Conclusions:

  • The developed computer vision system provides a highly accurate and reliable method for automatic chickpea variety classification.
  • The ensemble machine learning approach significantly enhances classification performance compared to individual classifiers.
  • This technology offers a potential solution to combat chickpea variety fraud and ensure agricultural product integrity.