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Vision-Based Apple Classification for Smart Manufacturing.

Ahsiah Ismail1, Mohd Yamani Idna Idris2, Mohamad Nizam Ayub3

  • 1Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia. ahsiahismail15@siswa.um.edu.my.

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Summary

This study introduces an accurate vision-based approach for smart manufacturing, using image recognition to classify defective and non-defective apples. Spatial Pyramid Matching achieved 98.15% accuracy, optimizing quality control.

Keywords:
Bag of WordsConvolutional Neural NetworkSpatial Pyramid Matchingimage recognitionsmart manufacturingvision sensor

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Smart Manufacturing

Background:

  • Smart manufacturing relies on data analytics for process optimization.
  • Accurate data capture is crucial for effective data analytics in production.

Purpose of the Study:

  • To propose an accurate data capture approach using vision sensors for smart manufacturing.
  • To evaluate and compare image recognition techniques for classifying defective and non-defective apples.

Main Methods:

  • Three image recognition methods were studied: Bag of Words (BOW), Spatial Pyramid Matching (SPM), and Convolutional Neural Network (CNN).
  • Classifiers were trained and evaluated using K-fold cross-validation on 550 apple images (275 defective, 275 non-defective).

Main Results:

  • Spatial Pyramid Matching (SPM) with an SVM classifier achieved 98.15% classification accuracy using 10-fold cross-validation.
  • Convolutional Neural Network (CNN) with an SVM classifier demonstrated the fastest computational time.

Conclusions:

  • SPM offers superior classification accuracy for defect detection in apples.
  • Both SPM and CNN provide efficient vision-based solutions for automated inspection and quality control in smart manufacturing.