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dc.contributor.authorMunera, Sandra
dc.contributor.authorGómez-Sanchís, Juan
dc.contributor.authorAleixos, Nuria
dc.contributor.authorVila-Francés, Joan
dc.contributor.authorColelli, Giancarlo
dc.contributor.authorCubero, Sergio
dc.contributor.authorSoler, Esteban
dc.contributor.authorBlasco, José
dc.date.accessioned2021-01-18T09:33:04Z
dc.date.available2021-01-18T09:33:04Z
dc.date.issued2021es
dc.identifier.citationMunera, S., Gómez-Sanchís, J., Aleixos, N., Vila-Francés, J., Colelli, G., Cubero, S., ... & Blasco, J. (2021). Discrimination of common defects in loquat fruit cv.‘Algerie’using hyperspectral imaging and machine learning techniques. Postharvest Biology and Technology, 171, 111356.es
dc.identifier.issn0925-5214
dc.identifier.urihttp://hdl.handle.net/20.500.11939/6973
dc.description.abstractLoquat (Eriobotrya japonica L.) is an important fruit for the economy of some regions of Spain that is very susceptible to mechanical damage and physiological disorders. These problems depreciate its value and prevent it from being exported. Visible (VIS) and near infrared (NIR) hyperspectral imaging was used to discriminate between external and internal common defects of loquat cv. ‘Algerie’. Two classifiers, random forest (RF) and extreme gradient boost (XGBoost), and different spectral pre-processing techniques were evaluated in terms of their capacity to distinguish between sound and defective features according to three approaches. In the first approach the fruit pixels were classified into two classes, sound or defect, with a 97.5% rate of success; in the second the defective features were considered internal or external defects, achieving a 96.7% rate of success; and in the third approach each type of defect, i.e. purple spot, bruising, scars and flesh browning, were considered separately with a correct classification rate of 95.9%. The results indicated that the XGBoost classifier was the best method in all cases.es
dc.language.isoenes
dc.publisherElsevieres
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectNon-destructivees
dc.subjectArtificial visiones
dc.titleDiscrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniqueses
dc.typearticlees
dc.authorAddressInstituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315, Km. 10’7, 46113 Moncada (Valencia), Españaes
dc.entidadIVIACentro de Agroingenieríaes
dc.identifier.doi10.1016/j.postharvbio.2020.111356es
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0925521420309285es
dc.journal.issueNumber171es
dc.journal.titlePostharvest Biology and Technologyes
dc.page.final111356es
dc.page.initial111356es
dc.rights.accessRightsopenAccesses
dc.source.typeelectronicoes
dc.subject.agrisH20 Plant diseaseses
dc.subject.agrisN01 Agricultural engineeringes
dc.subject.agrisN20 Agricultural machinery and equipmentes
dc.subject.agrovocEriobotrya japonicaes
dc.subject.agrovocQualityes
dc.subject.agrovocClassificationes
dc.subject.agrovocMultivariate analysises
dc.type.hasVersionacceptedVersiones


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Atribución-NoComercial-SinDerivadas 3.0 España
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