Identification of Damaged Kernels in Wheat using a Colour Machine Vision System

Abstract

A colour machine vision system was used for identification of healthy and six types of damaged kernels [broken, grass-green/green-frosted, black-point/smudged (selected from grain samples), mildewed, heated and bin-/fire-burnt (created in the laboratory)] of Canada Western Red Spring (CWRS) wheat. A software package was developed to extract various morphological and colour features from the images of both healthy and damaged kernels. Different feature models, morphological, colour and combined (morphological and colour), were evaluated for the identification analysis using the SAS procedures, STEPDISC and DISCRIM. Both parametric and non-parametric statistical classification methods were evaluated with the selected feature models. Colour features proved to be efficient for the identification of healthy and damaged kernels, while combining morphological with colour features improved the identification accuracy. Using a non-parametric classifier with a selected combined model of 24 colour and 4 morphological features, the average identification accuracies were: 93% (healthy), 90% (broken), 99% (grass-green/green-frosted), 99% (black-point/smudged), 99% (mildewed), 98% (heated), and 100% (bin-/fire-burnt), when trained and tested with three different training and testing data sets.