Plant recognition system based on extreme learning machine by using shearlet transform and new geometric features

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dc.authoridHanbay, Davut/0000-0003-2271-7865
dc.authorwosidHanbay, Davut/AAG-8511-2019
dc.contributor.authorTurkoglu, Muammer
dc.contributor.authorHanbay, Davut
dc.date.accessioned2024-08-04T20:09:59Z
dc.date.available2024-08-04T20:09:59Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractTo date, different approaches have been used to be correctly identified of plant species. Leaves are the most important approaches as part of the plants which provide many features with advantages such as shape, color and vein texture. In this study, a new approach based on the geometrical properties of the leaf has been proposed. This method called Edge Step (ES), consists of features such as angle, center-edge length and edge distance by using edge points in the shape boundary curve. In addition, Shearlet Transform method, which has features such as good sensitivity to tissue identification, rapid calculation and directional independence, is used. In addition to these methods, Color features and Gray-Level Co-Occurrence Matrix (GLCM) method to extract color and texture properties from leaf images have been applied. Attributes derived from all these methods were tested with the Extreme Learning Machine (ELM) classifier method as separately and combination. The proposed study has been tested by using four different plant leaf datasets such as Flavia, Swedish, ICL and Foliage. Using these datasets, studies based on texture, shape and color characteristics have been compared with the performance of the proposed approach. As a result, the proposed method is identified to be more successful than the other methods.en_US
dc.identifier.doi10.17341/gazimmfd.423674
dc.identifier.endpage2112en_US
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85069826759en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2097en_US
dc.identifier.trdizinid389839en_US
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.423674
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/389839
dc.identifier.urihttps://hdl.handle.net/11616/92540
dc.identifier.volume34en_US
dc.identifier.wosWOS:000486923100005en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isotren_US
dc.publisherGazi Univ, Fac Engineering Architectureen_US
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLeaf Recognitionen_US
dc.subjectImage Processingen_US
dc.subjectShearlet Transformen_US
dc.subjectEdge Step Methoden_US
dc.subjectExtreme Learning Machinesen_US
dc.titlePlant recognition system based on extreme learning machine by using shearlet transform and new geometric featuresen_US
dc.typeArticleen_US

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