GRAIN KERNEL IDENTIFICATION USING KERNEL SIGNATURE

Abstract

A machine vision algorithm was developed to distinguish the kernels of Canada Western Red Spring (CWRS)wheat, Canada Western Amber Durum (CWAD) wheat, barley, oats, and rye. The algorithm is based on the assumptionthat the shape of any of these kernels can be described by a radial function using the properties of Fourier descriptorswhich are invariant to translation, rotation, and scale. To evaluate the discrimination capability of the algorithm, colorimages of 2000 kernels for each type of grain (200 each from ten growing regions across Canada) were taken. For eachkernel, three attributes viz. length, shape function (Fourier descriptors in polar coordinates), and color were extractedwhich were collectively called the kernel signature. Each attribute was then averaged for each grain type to form atraining set. To identify any unknown kernel, all the three attributes were calculated for it and were compared against thecorresponding values for each grain type in the training set using three different distance functions one each for eachattribute. Classification was done by assigning different weights to the attributes, shape being the most important andlength being the least. Robustness of the algorithm was tested by taking the test kernels from growing regions alien to thetraining set. Classification accuracies of 100, 94, 93, 99, and 95% were obtained for CWRS wheat, CWAD wheat, barley,oats, and rye, respectively.