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Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection

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dc.contributor.authorPourdarbani, Raziyeh
dc.contributor.authorSabzi, Sajad
dc.contributor.authorZohrabi, Reihaneh
dc.contributor.authorGarcía Mateos, Ginés
dc.contributor.authorFernández Beltrán, Rubén
dc.contributor.authorMolina Martínez, José Miguel
dc.contributor.authorRohban, Mohammad H.
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-19T08:53:19Z
dc.date.available2026-01-19T08:53:19Z
dc.date.copyright© 2023 The Authors.
dc.date.issued2023-10-25
dc.description.abstractRecent advances in hyperspectral imaging (HSI) have demonstrated its ability to detect defects in fruit that may not be visible in RGB images. HSIs can be considered 3D images containing two spatial dimensions and one spectral dimension. Therefore, the first question that arises is how to process this type of information, either using 2D or 3D models. In this study, HSI in the 550–900 nm spectral range was used to detect bruising in oranges. Sixty samples of Thompson oranges were subjected to a mechanical bruising process, and HSIs were taken at different time intervals: before bruising, and 8 and 16 h after bruising. The samples were then classified using two convolutional neural network (CNN) models, a shallow 7-layer network (CNN-7) and a deep 18-layer network (CNN-18). In addition, two different input processing approaches are used: using 2D information from each band, and using the full 3D data from each HSI. The 3D models were the most accurate, with 94% correct classification for 3D-CNN-18, compared to 90% for 3D-CNN-7, and less than 83% for the 2D models. Our study suggests that 3D HSI may be a more effective technique for detecting fruit bruising, allowing the development of a fast, accurate, and nondestructive method for fruit sorting.
dc.formatapplication/pdf
dc.format.extent15
dc.identifier.citationJournal of Food Science, 2023, vol. 88, no 12, p. 5149-5163.
dc.identifier.doihttps://doi.org/10.1111/1750-3841.16801
dc.identifier.eissn1750-3841
dc.identifier.issn0022-1147
dc.identifier.urihttp://hdl.handle.net/10201/188030
dc.languageeng
dc.publisherWiley
dc.relationThis research was funded by project 22130/PI/22 from the Autonomous Community of the Region of Murcia, Spain, through the grants to projects for the development of scientific and technical research by competitive groups, included in the Regional Program for the Promotion of Scientific and Technical Research of Excellence (Action Plan 2022) of the “Fundación Séneca, Agencia de Ciencia y Tecnología de la Region de Murcia.”
dc.relation.publisherversionhttps://ift.onlinelibrary.wiley.com/doi/10.1111/1750-3841.16801
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBruise detection
dc.subjectComputer vision
dc.subjectDeep learning
dc.subjectHyperspectral imaging
dc.subjectVisible and near infrared spectroscopy
dc.subject.odsObjetivo 12: Producción y consumo sostenibles
dc.titleComparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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relation.isAuthorOfPublication4b1f10dc-5b0a-4f2b-b544-9b5a81e3a07e
relation.isAuthorOfPublication.latestForDiscoverybb68ac7e-f852-4bc6-a7b4-e9a37a5617c2
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