Visual discrimination between barley varieties is difficult, and it requires training and experience. The
development of automatic methods based on computer vision could have positive implications for the
food processing industry. In the brewing industry, varietal uniformity is crucial for the production of high
quality malt. The varietal purity of thousands of tons of grain has to be inspected upon purchase in the
malt house.
This paper evaluates the effectiveness of identification of barley varieties based on image-derived
shape, color and texture attributes of individual kernels. Varieties can be determined by means of
discriminant analysis, including reduction of feature space dimensionality, linear classifier ensembles
and artificial neural networks, with high balanced accuracy ranging from 67% to 86%. The study demonstrated
that classification results can be significantly improved by standardizing individual kernel images
in terms of their anteroposterior and dorsoventral orientation and performing additional analyses of
wrinkled regions.