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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Different cultivars of seeds may have different properties. Therefore, distinguishing cultivars may be important for seed processing and product quality. This study was aimed at revealing the usefulness of innovative models developed based on selected image textures built using traditional machine algorithms for cultivar classification of quince seeds. The quince seeds belonging to four cultivars ‘Uspiech’, ‘Leskovac’, ‘Bereczki’, and ‘Kaszczenko’ were considered. In total, 1629 image textures from different color channels for each seed were extracted from color images acquired using a flatbed scanner. Texture parameters were used to build models for a combined set of selected textures from all color channels, sets of selected textures from color spaces RGB, Lab, and XYZ, and individual color channels R, G, B, L, a, b, X, Y, and Z using algorithms from different groups. The most successful models were developed using the Logistic (group of Functions), IBk (Lazy), LogitBoost (Meta), LMT (Trees), and naïve Bayes (Bayes). The classification accuracy reached 98.75% in the case of a model based on a combined set of textures selected from images in all color channels developed using the Logistic algorithm. For most models, the greatest misclassification of cases was observed between seeds ‘Bereczki’ and ‘Kaszczenko’. The developed procedure can be used in practice to distinguish quince seeds in terms of a cultivar and avoid mixing seed cultivars with different properties intended for further processing.

Details

Title
Distinguishing Seed Cultivars of Quince (Cydonia oblonga Mill.) Using Models Based on Image Textures Built Using Traditional Machine Learning Algorithms
Author
Ropelewska, Ewa 1   VIAFID ORCID Logo  ; Kruczyńska, Dorota E 2   VIAFID ORCID Logo  ; Mieszczakowska-Frąc, Monika 1   VIAFID ORCID Logo 

 Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland; [email protected] 
 Cultivar Testing, Nursery and Gene Bank Resources Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland; [email protected] 
First page
1310
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20770472
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2842902467
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.