Classification of organic and conventional olives using convolutional neural networks

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dc.authoridunluturk, mehmet/0000-0003-1274-9361
dc.authorscopusid6508114835
dc.authorscopusid57225146769
dc.authorscopusid23968205700
dc.contributor.authorUnluturk, Mehmet S.
dc.contributor.authorKucukyasar, Secil
dc.contributor.authorPazir, Fikret
dc.date.accessioned2023-01-12T19:50:16Z
dc.date.available2023-01-12T19:50:16Z
dc.date.issued2021
dc.departmentN/A/Departmenten_US
dc.description.abstractThis paper presents a convolutional neural network (CNN) to classify between the conventionally and organically cultivated Memecik varieties of green olives. The image forming method called the rising paper chromatography is utilized in preparing the images of Memecik varieties of green olives for CNN. In the rising chromatography method, 20, 30, and 40% sample concentrations were determined as the suitable concentrations for both organic and conventional olives. The concentrations of AgNO3 and FeSO4 were determined as 0.25, 0.5, 0.75 and 1% for both conventional and organic samples. The visual differences used for differentiation of different types of Memecik green olives are usually determined according to the regional color differences, the vivid color occurrence, the width and the frequency of bowl occurrence, the thin line, and the picks at drop zone by the expert assessors. The testing results in this study verified the effectiveness of the CNN methodology in differentiating between the organically and conventionally cultivated Memecik green olives. The newly designed neural network achieved 100% accuracy. Furthermore, this high accuracy achieved by CNN might suggest that it can be effectively used in place of the expert assessors.en_US
dc.identifier.doi10.1007/s00521-021-06269-z
dc.identifier.endpage16744en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue23en_US
dc.identifier.scopus2-s2.0-85109296849en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage16733en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06269-z
dc.identifier.urihttps://hdl.handle.net/11454/76060
dc.identifier.volume33en_US
dc.identifier.wosWOS:000669289700003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOrganic oliveen_US
dc.subjectConventional oliveen_US
dc.subjectMemeciken_US
dc.subjectRising paper chromatographyen_US
dc.subjectConvolutional neural networken_US
dc.subjectQualityen_US
dc.subjectSystemsen_US
dc.subjectFruitsen_US
dc.titleClassification of organic and conventional olives using convolutional neural networksen_US
dc.typeArticleen_US

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