<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus <italic>Fusarium</italic> and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100%. The lowest result (95%) was noted in XYZ color model and Multi Class Classifier for the textures selected using the Ranker method and the OneR attribute evaluator. Selected classifiers were characterized by 100% accuracy in the case of all color models and selection methods. The highest number of 100% results was obtained for the Lab color model with Naive Bayes, LDA, IBk, Multi Class Classifier and J48 classifiers in the Best First selection method with the CFS subset evaluator.</p></abstract>

The aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus Fusarium and healthy kernels. Infected barley kernels with altered shape and discoloration and healthy barley kernels were scanned. Textures were computed using MaZda software. The kernels were classified as infected and healthy with the use of the WEKA application. In the case of RGB, Lab and XYZ color models, the classification accuracies based on 10 selected textures with the highest discriminative power ranged from 95 to 100%. The lowest result (95%) was noted in XYZ color model and Multi Class Classifier for the textures selected using the Ranker method and the OneR attribute evaluator. Selected classifiers were characterized by 100% accuracy in the case of all color models and selection methods. The highest number of 100% results was obtained for the Lab color model with Naive Bayes, LDA, IBk, Multi Class Classifier and J48 classifiers in the Best First selection method with the CFS subset evaluator.

The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus <italic>Fusarium</italic>

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 Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus 

The aim of this study was to develop discrimination models based on textural features for the identification of barley kernels infected with fungi of the genus...

The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium

Data publikacji: 16 paź 2018

Zakres stron: 49 - 56

Otrzymano: 01 cze 2018

Przyjęty: 01 sie 2018

DOI: https://doi.org/10.1515/agriceng-2018-0026

Słowa kluczowe
barley kernels, fungal infection, textures, discriminative classifiers

© 2018 Ewa Ropelewska, published by Sciendo

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

The Application of Computer Image Analysis Based on Textural Features for the Identification of Barley Kernels Infected with Fungi of the Genus Fusarium

Data publikacji: 16 paź 2018

Zakres stron: 49 - 56

Otrzymano: 01 cze 2018

Przyjęty: 01 sie 2018

DOI: https://doi.org/10.1515/agriceng-2018-0026

Słowa kluczowebarley kernels, fungal infection, textures, discriminative classifiers

© 2018 Ewa Ropelewska, published by Sciendo

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

Słowa kluczowe
barley kernels, fungal infection, textures, discriminative classifiers