Abstract
Nowadays, with the rapid development of digital image processing, there has been a notable increase in elaborating advanced tools for studying the internal structure of objects. This may be very helpful in characterizing certain morphological traits of grains, as well as in quantifying the differences between them. The current research was carried out to study the structure of the traits and to determine their importance in relation to grain classification and identification. To achieve better performance and deeper understanding of their usefulness, the investigation was done by means of both principal component analysis and multivariate factor analysis. Herein, the percentage of variation explained by the first three factors reached a high 89.97%. Thus, the presented methodology supported reliable discrimination of the wheat varieties as regards their shape descriptors. The conducted study confirmed the practical usefulness and effectiveness of the evolved method when applied to the many practical tasks wherein the image analysis commonly employed in multivariate statistical methods is recommended.
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