<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The aim of this study was to perform and evaluate the accuracy of classification of grains of different cultivars of malting barley. The grains of eight cultivars: <italic>Blask, Bor do, Con chita, Kormoran, Mercada, Serwal, Signora, Victoriana,</italic> with three moisture content: 12, 14, 16% were examined. The selected parameters of the surface texture of grain mass obtained from images taken using the techniques of hyperspectral imaging were determined. The accuracy of grains discrimination carried out using different methods of selection and classification of data was compared. The pairwise comparison and comparison of three, four and eight cultivars of malting barley were carried out. The most accurate discrimination was determined in the case of the pairwise comparison. <italic>Victoriana</italic> cultivar was the most different from the others. The most similar texture of grain mass was found in the comparison of cultivars: <italic>Blask</italic> and <italic>Mercada.</italic> In the case of eight examined cultivars of malting barley, the most accurate discrimination (classification error – 55%) was obtained for images taken at the moisture content of 14% and at a wavelength of 750 nm, for the attributes selection performed with the use of probability of error and average correlation coefficient (POE+ACC) method and the discrimination carried out using the linear discriminant analysis (LDA).</p></abstract>

The aim of this study was to perform and evaluate the accuracy of classification of grains of different cultivars of malting barley. The grains of eight cultivars: Blask, Bor do, Con chita, Kormoran, Mercada, Serwal, Signora, Victoriana, with three moisture content: 12, 14, 16% were examined. The selected parameters of the surface texture of grain mass obtained from images taken using the techniques of hyperspectral imaging were determined. The accuracy of grains discrimination carried out using different methods of selection and classification of data was compared. The pairwise comparison and comparison of three, four and eight cultivars of malting barley were carried out. The most accurate discrimination was determined in the case of the pairwise comparison. Victoriana cultivar was the most different from the others. The most similar texture of grain mass was found in the comparison of cultivars: Blask and Mercada. In the case of eight examined cultivars of malting barley, the most accurate discrimination (classification error – 55%) was obtained for images taken at the moisture content of 14% and at a wavelength of 750 nm, for the attributes selection performed with the use of probability of error and average correlation coefficient (POE+ACC) method and the discrimination carried out using the linear discriminant analysis (LDA).

Application of Hyperspectral Imaging for Cultivar Discrimination of Malting Barley Grains

Department of Systems Engineering, Faculty of Engineering, University of Warmia and Mazury in Olsztyn

Agricultural Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The aim of this study was to perform and evaluate the accuracy of classification of grains of different cultivars of malting barley. The grains of eight...

Application of Hyperspectral Imaging for Cultivar Discrimination of Malting Barley Grains

Published Online: Oct 17, 2016

Page range: 207 - 217

Received: Feb 01, 2016

Accepted: Apr 01, 2016

DOI: https://doi.org/10.1515/agriceng-2016-0058

Keywords
spring malting barley, grain mass, attributes selection, multidimensional analysis, classification error

© 2016 Piotr Zapotoczny et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Application of Hyperspectral Imaging for Cultivar Discrimination of Malting Barley Grains

Published Online: Oct 17, 2016

Page range: 207 - 217

Received: Feb 01, 2016

Accepted: Apr 01, 2016

DOI: https://doi.org/10.1515/agriceng-2016-0058

Keywordsspring malting barley, grain mass, attributes selection, multidimensional analysis, classification error

© 2016 Piotr Zapotoczny et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
spring malting barley, grain mass, attributes selection, multidimensional analysis, classification error