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Convolutional neural networks for estimating the ripening state of fuji apples using visible and near-infrared spectroscopy

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dc.contributor.authorBenmouna, Brahim
dc.contributor.authorGarcía Mateos, Ginés
dc.contributor.authorSabzi, Sajad
dc.contributor.authorFernández Beltrán, Rubén
dc.contributor.authorParras Burgos, Dolores
dc.contributor.authorMolina Martínez, José Miguel
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-19T09:12:46Z
dc.date.available2026-01-19T09:12:46Z
dc.date.copyright© The Author(s) 2022
dc.date.issued2022-07-18
dc.description.abstractThe quality of fresh apple fruits is a major concern for consumers and manufacturers. Classification of these fruits according to their ripening stage is one of the most decisive factors in determining their quality. In this regard, the aim of this work is to develop a new method for non-destructive classification of the ripening state of Fuji apples using hyperspectral information in the visible and near-infrared (Vis/NIR) regions. Spectra of 172 apple samples in the range from 450 to 1000 nm were studied, which were selected from four different ripening stages. A convolutional neural network (CNN) model was proposed to perform the classification of the samples. The proposed method was compared with three alternative methods based on artificial neural networks (ANN), support vector machines (SVM), and k-nearest neighbors (KNN). The results revealed that the CNN method outperformed the alternative methods, achieving a correct classification rate (CCR) of 96.5%, compared with an average of 89.5%, 95.93%, and 91.68% for ANN, SVM, and KNN, respectively. These results will help in the development of a new device for fast and accurate estimation of the quality of apples.
dc.formatapplication/pdf
dc.format.extent11
dc.identifier.citationFood and Bioprocess Technology, 2022, vol. 15, no 10, p. 2226-2236.
dc.identifier.doihttps://doi.org/10.1007/s11947-022-02880-7
dc.identifier.eissn1935-5149
dc.identifier.issn1935-5130
dc.identifier.urihttp://hdl.handle.net/10201/188209
dc.languageeng
dc.publisherSpringer
dc.relationOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by grant RTI2018-098156-B-C53 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe.”
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s11947-022-02880-7
dc.rightsAttribution 4.0 International
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectFuji apples
dc.subjectSpectroscopy
dc.subjectDeep learning
dc.subjectNeural networks
dc.subject.odsObjetivo 2: Hambre y seguridad alimentaria
dc.subject.odsObjetivo 9: Infraestructura
dc.subject.odsObjetivo 12: Producción y consumo sostenibles
dc.titleConvolutional neural networks for estimating the ripening state of fuji apples using visible and near-infrared spectroscopy
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
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relation.isAuthorOfPublication4b1f10dc-5b0a-4f2b-b544-9b5a81e3a07e
relation.isAuthorOfPublication.latestForDiscovery4b1f10dc-5b0a-4f2b-b544-9b5a81e3a07e
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