Characterization of the influence of moisture content on the morphological features of single wheat kernels using machine vision

Ganesan Ramalingam, Suresh Neethirajan, Digvir S. Jayas, Noel D.G. White

Research output: Contribution to journalArticleAcademicpeer-review

5 Citations (Scopus)

Abstract

The objective of this study was to quantify changes in morphological features of kernels of western Canadian wheat classes caused by moisture increase using a machine vision system. One hundred single wheat kernels for each of eight western Canadian wheat classes were successively conditioned from 12% to 20% (wet basis) moisture contents using potassium hydroxide (KOH) concentrations which regulated relative humidity. A digital camera of 7.4 × 7.4-μm pixel resolution with an inter-line transfer charge-coupled device (CCD) image sensor was used to acquire images of single kernels. A machine vision algorithm developed at the Canadian Wheat Board Centre for Grain Storage Research, University of Manitoba, was implemented to extract seven morphological features (area, perimeter, major axis length, minor axis length, maximum radius, minimum radius, and mean radius) from the wheat kernel images. All the seven features of Canada Western Red Spring, Canada Western Amber Durum, Canada Prairie Spring White, Canada Prairie Spring Red, Canada Western Extra Strong, Canada Western Red Winter, Canada Western Hard White Spring, and Canada Western Soft White Spring wheat kernels were significantly (a = 0.05) different as the moisture content increased from 12% to 20%. All seven features showed a linearly increasing trend with an increase in moisture content.

Original languageEnglish
Pages (from-to)403-409
Number of pages7
JournalApplied Engineering in Agriculture
Volume27
Issue number3
DOIs
Publication statusPublished - 2011
Externally publishedYes

Keywords

  • Machine vision
  • Moisture content
  • Morphological features
  • Single wheat kernels

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