Discrimination of Wheat Grain Varieties Using Image Analysis and Multidimensional Analysis Texture of Grain Mass.

This article presents the results of discrimination of 11 wheat grain varieties. The statistical analysis included reduction of variables to a set of 49 textures with the highest discriminating strength and multidimensional analysis. Reduction of variables was performed by the following methods: genetic algorithms (sequential forward floating search [SFFS] method) as well as theClass RankerandClass RankersSearchmethods. Furthermore, the multidimensional analysis was performed by methods employing the following classifiers:Bayes, Lazy, Meta, Decision trees,andDiscriminatory analyses.The classification of individual varieties, regardless of the year of cultivation, was between 98 and 100%. [ABSTRACT FROM AUTHOR]