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Image processing techniques to estimate weight and morphological parameters for selected wheat refractions.

Rohit Sharma1, Mahesh Kumar2, M S Alam2

  • 1Department of Processing and Food Engineering, Punjab Agricultural University, Ludhiana, Punjab, India. mail2rohit704@gmail.com.

Scientific Reports
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

Image processing accurately measures wheat grain physical properties like size, shape, and weight. This method shows strong linear relationships with manual measurements, aiding in grain quality assessment and classification.

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Area of Science:

  • Agricultural Science
  • Image Processing
  • Food Quality Assessment

Background:

  • Physical characteristics of agricultural materials are vital for quality assessment.
  • Traditional methods for measuring grain properties can be labor-intensive and subjective.

Purpose of the Study:

  • To classify physical characteristics of wheat grains (sound, damaged, shriveled, broken) using image processing.
  • To establish relationships between manual measurements and image processing techniques for weight and volume.
  • To explore the potential of image analysis for automated grain quality evaluation.

Main Methods:

  • Acquisition of wheat grain images using a flatbed scanner.
  • Digital image processing techniques to analyze size, shape, color, and texture.
  • Comparison of image analysis results with manual measurements using a digital vernier caliper.
  • Extraction of color and texture features using Python and OpenCV.

Main Results:

  • Strong linear relationships (R² 0.798-0.947) found between manual and image-based axial dimensions.
  • Linear correlation (R² 0.841-0.920) between individual kernel weight and projected area.
  • Consistent findings on grain shape (ellipsoid with convex geometry) between methods.
  • Strong linear relationships (R² 0.845-0.945) for grain volume estimation.

Conclusions:

  • Digital image processing provides a reliable and accurate method for assessing wheat grain physical properties.
  • The established relationships facilitate the development of automated systems for grain quality classification.
  • Extracted image features can be utilized with machine and deep learning algorithms for advanced grain analysis.