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A malt is one of intermediate ingredients for a brewing industry. The quality of barley used for malting have essential impact on the final product flavor. An automatic system for a barley grains inspection, utilizing computer vision methods, can provide an objective quality assessment. We present image preprocessing steps of grain inspection system. Main preprocessing steps are: segmentation of grain kernel images, identification of dorsal and ventral sides of the kernels, aligning them with respect to the germ-brush direction. The results of preprocessing are presented and discussed.

Qmazda is a package of software tools for digital image analysis. They compute shape, color and texture attributes in arbitrary regions of interest, implement selected algorithms of discriminant analysis and machine learning, and enable texture based image segmentation. The algorithms generalize a concept of texture to three-dimensional data to enable analysis of volumetric images from magnetic resonance imaging or computed tomography scanners. The tools support a complete workflow — from image examples as an input to classification rules as an output. The extracted knowledge can be further used in custom made image analysis systems. Here we also present an application of QMaZda to identify defective barley kernels. The cereal seeds variability is high, therefore, characterization and discriminant analysis of such the biological objects is challenging and non-trivial. The software is available free of charge and open source, with executables for Windows, Linux and OS X platforms.