Automatic classification of non-touching cereal grains in digital images using limited morphological and color features

Classification of cereal grains, namely; barley, oat, rye and wheat (Canada Western Amber Durum (CWAD) and Canada Western Red Spring (CWRS)) was performed using morphological and color features. Grain image boundary contours were extracted from the digital images of kernels, expressed as chain-coded...

Full description

Saved in:
Bibliographic Details
Published in:Computers and electronics in agriculture 2013, Vol.90, p.99-105
Main Authors: Mebatsion, H.K, Paliwal, J, Jayas, D.S
Format: Article
Language:English
Subjects:
Agronomy. Soil science and plant productions
Automated discrimination
barley
Biological and medical sciences
Cereal grain classification
Cereals
Classification
Color
digital images
durum wheat
Fundamental and applied biological sciences. Psychology
Grains
Kernels
Machine vision
Mathematical models
oats
Rye
seeds
Shape
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Classification of cereal grains, namely; barley, oat, rye and wheat (Canada Western Amber Durum (CWAD) and Canada Western Red Spring (CWRS)) was performed using morphological and color features. Grain image boundary contours were extracted from the digital images of kernels, expressed as chain-coded points and then approximated by 13 elliptic Fourier coefficients. After normalization of the rotation and starting point of the contours, symmetrical standard coefficients were determined. The symmetrical Fourier index (SFX) of individual kernels was calculated from the product of the sum of absolute symmetrical coefficients and the circularity (roundness) index. Three geometric features, namely; aspect ratio (AR), major diameter (MD) and roundness (Cₑq) were determined using ellipse fitting and Green’s transformation of curve integrals, respectively. The morphological classification model was defined using SFX, AR, MD, and Cₑq. The color classification model was defined using color indices of individual kernels, which were calculated from the RGB color values of their images. The classification accuracies of different models were evaluated and compared. The combined model defined by morphological and color features achieved a classification accuracy of 98.5% for barley, 99.97% for CWRS, 99.93% for oat, and 100% for rye and CWAD.
ISSN:0168-1699
1872-7107
DOI:10.1016/j.compag.2012.09.007