Differentiation of yeast-inoculated and uninoculated tomatoes using fluorescence spectroscopy combined with machine learning

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dc.authorid0000-0003-0238-9606en_US
dc.authorid0000-0001-7549-0137en_US
dc.contributor.authorRopelewska, Ewa
dc.contributor.authorSlavova, Vanya
dc.contributor.authorSabancı, Kadir
dc.contributor.authorAslan, Muhammet Fatih
dc.contributor.authorMasheva, Veselina
dc.contributor.authorPetkova, Mariana
dc.date.accessioned2022-12-23T06:04:24Z
dc.date.available2022-12-23T06:04:24Z
dc.date.issued2022en_US
dc.departmentKMÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000894306900001en_US
dc.description.abstractArtificial-intelligence-based analysis methods can provide objective and accurate results. This study aimed to evaluate the performance of machine learning algorithms to classify yeast-inoculated and uninoculated tomato samples using fluorescent spectroscopic data. For this purpose, three different tomato types were used: 'local dwarf', 'Picador', and 'Ideal'. Discrimination analysis was applied with six different machine learning (ML) algorithms. Confusion matrices, average accuracies, F-Measure, Precision, ROC (receiver operating characteristic) Area, MCC (Matthews Correlation Coefficient), and precision-recall area values obtained as a result of the application of different ML algorithms were compared. Based on the fluorescence spectroscopic data, the application of six ML algorithms showed that the first two tomato types were classified with 100% accuracy and the last type was classified with 95% accuracy. The results of the study show that the fluorescence spectroscopy data are strongly representative of tomato species. ML methods fed with these data provide high-performance discrimination.en_US
dc.identifier.citationRopelewska, E., Slavova, V., Sabancı, K., Aslan, M. F., Masheva, V., Petkova, M. (2022). Differentiation of yeast-inoculated and uninoculated tomatoes using fluorescence spectroscopy combined with machine learning. Agriculture-Basel, 12, 11.en_US
dc.identifier.doi10.3390/agriculture12111887
dc.identifier.issn2077-0472
dc.identifier.issue11en_US
dc.identifier.scopus2-s2.0-85144949607
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/agriculture12111887
dc.identifier.urihttps://hdl.handle.net/11492/6902
dc.identifier.volume12en_US
dc.identifier.wosWOS:000894306900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Sceince
dc.indekslendigikaynakScopus
dc.institutionauthorSabancı, Kadir
dc.institutionauthorAslan, Muhammet Fatih
dc.language.isoen
dc.publisherMDPIen_US
dc.relation.journalAgriculture-Baselen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectYeast-Inoculated Tomatoen_US
dc.subjectFluorescence Spectroscopic Dataen_US
dc.subjectMachine Learning Algorithmsen_US
dc.subjectClassification Metricsen_US
dc.titleDifferentiation of yeast-inoculated and uninoculated tomatoes using fluorescence spectroscopy combined with machine learningen_US
dc.typeArticle

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