Identifying barley varieties by computer vision

• Methodology for barley grain classification by computer vision.
• Number of texture, color and shape features optimal for varietal classification was determined.
• Identification of anteroposterior and dorsoventral kernel orientation improves classification results.
• Additional analysis of kernel wrinkled region improves results of varietal identification.

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.