Publication:
Discriminating rapeseed varieties using computer vision and machine learning

dc.contributor.buuauthorKurtulmuş, Ferhat
dc.contributor.buuauthorÜnal, Halil
dc.contributor.departmentZiraat Fakültesi
dc.contributor.departmentBiyosistem Mühendisliği Bölümü
dc.contributor.researcheridR-8053-2016
dc.contributor.researcheridAAH-4410-2021
dc.contributor.scopusid15848202900
dc.contributor.scopusid55807866400
dc.date.accessioned2022-06-10T06:18:04Z
dc.date.available2022-06-10T06:18:04Z
dc.date.issued2015-03
dc.description.abstractRapeseed is widely cultivated throughout the world for the production of animal feed, vegetable fat for human consumption, and biodiesel. Since the seeds are evaluated in many areas for sowing and oilseed processing, they must be identified quickly and accurately for selection of a correct variety. An affordable method based on computer vision and machine learning was proposed to classify the seven rapeseed varieties. Different types of feature sets, feature models, and machine learning classifiers were investigated to obtain the best predictive model for rapeseed classification. The training and test sets were used to tune the model parameters during the training epochs by varying the complexity of the predictive models with grid-search and K-fold cross validation. After obtaining optimized models for each level of complexity, a dedicated validation set was used to validate predictive models. The developed computer vision system provided an overall accuracy rate of 99.24% for the best predictive model in discriminating rapeseed variety.
dc.identifier.citationKurtulmuş, F. ve Ünal, H. (2015). "Discriminating rapeseed varieties using computer vision and machine learning". Expert Systems with Applications, 42(4), 1880-1891.
dc.identifier.endpage1891
dc.identifier.issn0957-4174
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84910662280
dc.identifier.startpage1880
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2014.10.003
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417414006265
dc.identifier.urihttp://hdl.handle.net/11452/27020
dc.identifier.volume42
dc.identifier.wos000347579500011
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherPergamon-Elsevier
dc.relation.journalExpert Systems with Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectMachine learning
dc.subjectRapeseed
dc.subjectVariety discrimination
dc.subjectColor texture features
dc.subjectMechanical-properties
dc.subjectClassification
dc.subjectIdentification
dc.subjectRecognition
dc.subjectComputer science
dc.subjectEngineering
dc.subjectOperations research & management science
dc.subjectArtificial intelligence
dc.subjectLearning systems
dc.subjectOilseeds
dc.subjectComputer vision system
dc.subjectLearning classifiers
dc.subjectOil-seed processing
dc.subjectOverall accuracies
dc.subjectPredictive modeling
dc.subjectPredictive models
dc.subjectRapeseed
dc.subjectVariety discriminations
dc.subjectComputer vision
dc.subject.scopusCorn Ears; Seed; Computer Vision
dc.subject.wosComputer science, artificial intelligence
dc.subject.wosEngineering, electrical & electronic
dc.subject.wosOperations research & management science
dc.titleDiscriminating rapeseed varieties using computer vision and machine learning
dc.typeArticle
dc.wos.quartileQ1
dspace.entity.typePublication
local.contributor.departmentZiraat Fakültesi/Biyosistem Mühendisliği Bölümü
local.indexed.atScopus
local.indexed.atWOS

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