Evaluation of different pattern recognition techniques for apple sorting

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dc.contributor.authorKavdir, I.
dc.contributor.authorGuyer, D. E.
dc.date.accessioned2025-01-27T20:43:46Z
dc.date.available2025-01-27T20:43:46Z
dc.date.issued2008
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractGolden Delicious apples were classified using parametric and non-parametric classifiers into three quality classes. The features used in classification of apples were hue angle (for colour), shape defect, circumference, firmness, weight, blush percentage (red natural spots on the surface of the apple), russet (natural netlike formation on the surface of an apple), bruise content and number of natural defects. Different feature sets including four, five and nine features were also tested to find out the best classifier and feature set combination for an optimal classification success. The effects of using different feature sets and classifiers on classification performance were investigated. The feature set including five features produced slightly better classification results in general compared to feature sets including four and nine features. When the classifiers were compared, it was determined that the multi-layer perceptron neural network produced the highest classification results (up to 90%) while 1 - nearest- neighbour and 2-nearest-neighbour classifiers followed this classifier with an 81.11% classification success. The 3-nearest-neighbour and decision tree classifiers resulted in similar classification success (75.56%). The parametric plug-in decision rule classification resulted in the lowest classification success. Principal component analysis and linear discriminant analysis techniques were applied on the training data with nine, five and four features to visualise the degree of separation of the three quality classes of apples. As a result of this application, some improvements were observed in separation of the three quality classes from using four input features to nine features especially using principal components although some overlaps still existed among the classes. (C) 2007 IAgrE. Published by Elsevier Ltd. All rights reserved.
dc.identifier.doi10.1016/j.biosystemseng.2007.09.019
dc.identifier.endpage219
dc.identifier.issn1537-5110
dc.identifier.issue2
dc.identifier.scopus2-s2.0-38549101270
dc.identifier.scopusqualityQ1
dc.identifier.startpage211
dc.identifier.urihttps://doi.org/10.1016/j.biosystemseng.2007.09.019
dc.identifier.urihttps://hdl.handle.net/20.500.12428/24364
dc.identifier.volume99
dc.identifier.wosWOS:000254370900007
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcademic Press Inc Elsevier Science
dc.relation.ispartofBiosystems Engineering
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectMachine Vision
dc.subjectClassification
dc.subjectFruit
dc.subjectQuality
dc.subjectModels
dc.titleEvaluation of different pattern recognition techniques for apple sorting
dc.typeArticle

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