A Novel Convolutional-Recurrent Hybrid Network for Sunn Pest-Damaged Wheat Grain Detection

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dc.contributor.authorSabanci, Kadir
dc.contributor.authorAslan, Muhammet Fatih
dc.contributor.authorRopelewska, Ewa
dc.contributor.authorUnlersen, Muhammed Fahri
dc.contributor.authorDurdu, Akif
dc.date.accessioned2024-02-23T13:59:55Z
dc.date.available2024-02-23T13:59:55Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description.abstractThe sunn pest-damaged (SPD) wheat grains negatively affect the flour quality and cause yield loss. This study focuses on the detection of SPD wheat grains using deep learning. With the created image acquisition mechanism, healthy and SPD wheat grains are displayed. Image preprocessing steps are applied to the captured raw images, then data augmentation is performed. The augmented image data is given as an input to two different deep learning architectures. In the first architecture, transfer learning application is made using AlexNet. The second architecture is a hybrid structure, obtained by adding the bidirectional long short-term memory (BiLSTM) layer to the first architecture. In terms of accuracy, the performance of the non-hybrid and hybrid architectures that are presented in the study is determined as 98.50% and 99.50%, respectively. High classification success and innovative deep learning structure are the features of this study that distinguish it from previous studies.en_US
dc.identifier.doi10.1007/s12161-022-02251-0
dc.identifier.endpage1760en_US
dc.identifier.issn1936-9751
dc.identifier.issn1936-976X
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85125643313en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1748en_US
dc.identifier.urihttps://doi.org/10.1007/s12161-022-02251-0
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11356
dc.identifier.volume15en_US
dc.identifier.wosWOS:000764449400001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofFood Analytical Methodsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlexneten_US
dc.subjectLstmen_US
dc.subjectBilstmen_US
dc.subjectSunn Pest Damaged Wheaten_US
dc.subjectTransfer Learningen_US
dc.subjectWheat Classificationen_US
dc.titleA Novel Convolutional-Recurrent Hybrid Network for Sunn Pest-Damaged Wheat Grain Detectionen_US
dc.typeArticleen_US

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