Exploring Hu’s moment invariants and Zernike moments for effective identification of two-row and six-row barley varieties

Karolina Szturo

University of Warmia and Mazury in Olsztyn


Abstract

Barley variety identification is a complex and economically significant task. The identification of two-row and six-row grains is particularly important due to their different characteristics, such as protein and starch content, which have specific implications in different applications. This paper evaluates the effectiveness of discriminating between two-row and six-row barley grains using Hu moment invariants and Zernike moments in combination with various classifiers including linear and SVM classifiers with linear, RBF, polynomial, and sigmoid kernels. The application of Zernike moments and an SVM classifier using an RBF kernel achieved an accuracy level of 99.2%. In comparison, the application of Hu’s moment invariants resulted in an accuracy of 98.5%.


Keywords:

Zernike moments, Hu moment invariants, Image analysis, Barley grain

Supporting Agencies

The author would like to express their gratitude to the Polish Ministry of Science and Higher Education, and the National Centre of Research and Development for providing financial support for the project PBS3/A8/38/2015 – Development of industrial methods of automatic evaluation of technological parameters and classification of grain using image analysis.


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Published
2024-05-14

Cited by

Szturo, K. (2024). Exploring Hu’s moment invariants and Zernike moments for effective identification of two-row and six-row barley varieties . Technical Sciences, 27(27), 55–69. https://doi.org/10.31648/ts.10106

Karolina Szturo 
University of Warmia and Mazury in Olsztyn



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