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Early bread mold detection through microscopic images using convolutional neural network.

Panisa Treepong1, Nawanol Theera-Ampornpunt1

  • 1College of Computing, Prince of Songkla University, Phuket, Thailand.

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Detecting early bread mold is challenging. This study introduces a smartphone-based method using clip-on lenses and convolutional neural networks (CNNs) for accurate, early mold detection on bread.

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Area of Science:

  • Food science
  • Microbiology
  • Computer vision

Background:

  • Early-stage mold growth on bread is visually undetectable.
  • Current spoilage detection methods (visual inspection, expiration dates) are unreliable.
  • Existing microbial detection technologies are not consumer-friendly.

Purpose of the Study:

  • To develop a novel, consumer-accessible method for early bread mold detection.
  • To utilize smartphone microscopy and deep learning for automated spoilage identification.

Main Methods:

  • Capturing microscopic bread images (50× magnification) using smartphone clip-on lenses.
  • Employing convolutional neural networks (CNNs) with transfer learning for image classification.
  • Comparing various image preprocessing, CNN models, and data augmentation techniques.

Main Results:

  • Achieved high classification accuracy for early mold detection.
  • Near-perfect scores of 0.9948 for white sandwich bread.
  • Near-perfect scores of 0.9972 for whole wheat bread.

Conclusions:

  • The proposed method offers an effective and low-cost solution for early bread mold detection.
  • Smartphone-based microscopy combined with CNNs provides a viable tool for consumers.
  • This technology can enhance food safety and reduce food waste.