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Identification of Internal Defects in Potato Using Spectroscopy and Computational Intelligence Based on Majority

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This study developed a spectral analysis method to detect internal potato defects, achieving high accuracy using specific wavelengths and linear discriminant analysis (LDA). This technology enhances potato quality control and reduces disease susceptibility.

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Area of Science:

  • Agricultural Engineering
  • Spectroscopy
  • Machine Learning

Background:

  • Potatoes are a vital global food source, but internal defects reduce marketability and increase disease risk.
  • Current quality assessment methods often overlook internal defects in visually healthy potatoes.

Purpose of the Study:

  • To develop a non-destructive method for identifying internal defects in potatoes.
  • To select optimal spectral wavelengths for defect detection using machine learning.

Main Methods:

  • Utilized visible (Vis), near-infrared (NIR), and short-wavelength infrared (SWIR) spectroscopy to collect spectral data.
  • Employed hybrid artificial neural networks (ANN) and cultural algorithms (CA) for optimal wavelength selection.
  • Classified potato samples using an ensemble of classifiers (ANN-ICA, ANN-HS, LDA, KNN) with majority voting.

Main Results:

  • Optimal wavelengths were identified in both Vis/NIR and SWIR regions.
  • The ensemble method achieved high classification rates, with SWIR spectral data yielding 96.3% accuracy.
  • Linear Discriminant Analysis (LDA) using selected SWIR wavelengths achieved the highest accuracy at 97.7%.

Conclusions:

  • Spectral analysis combined with machine learning effectively detects internal potato defects.
  • Selected optimal wavelengths and LDA provide a highly accurate and efficient method for potato quality assessment.
  • This approach can significantly improve potato marketability and reduce post-harvest losses.