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dc.contributor.author | Gomez-Sanchis, J. | es_ES |
dc.contributor.author | Blasco, J. | es_ES |
dc.contributor.author | Soria-Olivas, E. | es_ES |
dc.contributor.author | Lorente, D. | es_ES |
dc.contributor.author | Escandell-Montero, P. | es_ES |
dc.contributor.author | Martinez-Martinez, J. M. | es_ES |
dc.contributor.author | Martinez-Sober, M. | es_ES |
dc.contributor.author | Aleixos Borrás, María Nuria | es_ES |
dc.date.accessioned | 2020-09-19T03:34:07Z | |
dc.date.available | 2020-09-19T03:34:07Z | |
dc.date.issued | 2013-08 | es_ES |
dc.identifier.issn | 0925-5214 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/150434 | |
dc.description.abstract | [EN] Green mold (Penicillium digitatum) and blue mold (Penicillium italicum) are important sources of postharvest decay affecting the commercialization of mandarins. These fungi infections produce enormous economic losses in mandarin production if early detection is not carried out. Nowadays, this detection is performed manually in dark chambers, where the fruit is illuminated by ultraviolet light to produce fluorescence, which is potentially dangerous for humans. This paper documents a new methodology based on hyperspectral imaging and advanced machine-learning techniques (artificial neural networks and classification and regression trees) for the segmentation and classification of images of citrus free of damage and affected by green mold and blue mold. Feature selection methods are used in order to reduce the dimensionality of the hyperspectral images and determine the 10 most relevant. Neural Networks were used to segment the hyperspectral images. Result's achieved using classifiers based on decision trees show an accuracy of around 93% in the problem of decay classification. | es_ES |
dc.description.sponsorship | This work was partially funded by the University of Valencia through project UV-INV-AE11-41271 and by the Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria de Espana (INIA) through research projects RTA2012-00062-C04-01 and RTA2012-00062-C04-03 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Postharvest Biology and Technology | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Fruit inspection | es_ES |
dc.subject | Mandarins | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Hyperspectral imaging | es_ES |
dc.subject | Machine vision | es_ES |
dc.subject | Image analysis | es_ES |
dc.subject | Non-linear classifiers | es_ES |
dc.subject | Decay | es_ES |
dc.subject.classification | EXPRESION GRAFICA EN LA INGENIERIA | es_ES |
dc.title | Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.postharvbio.2013.02.011 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/UV//INV-AE11-41271/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-01/ES/Nuevas técnicas de inspección basadas en espectrometría para la estimación de propiedades y determinación automática de la calidad interna y sanidad de productos agroalimentarios aplicadas a líneas de inspección y manipulación (SPEC-DACSA)/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//RTA2012-00062-C04-03/ES/Nuevas técnicas de inspección basadas en visión por computador multiespectral para la estimación de propiedades y determinación automática de la calidad y sanidad de la producción agroalimentaria en líneas de inspección y manipulación (VIS-DACSA)/ | es_ES |
dc.rights.accessRights | Cerrado | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Mecanización y Tecnología Agraria - Departament de Mecanització i Tecnologia Agrària | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica | es_ES |
dc.description.bibliographicCitation | Gomez-Sanchis, J.; Blasco, J.; Soria-Olivas, E.; Lorente, D.; Escandell-Montero, P.; Martinez-Martinez, JM.; Martinez-Sober, M.... (2013). Hyperspectral LCTF-based system for classification of decay in mandarins caused by Penicillium digitatum and Penicillium italicum using the most relevant bands and non-linear classifiers. Postharvest Biology and Technology. 82:76-86. https://doi.org/10.1016/j.postharvbio.2013.02.011 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.postharvbio.2013.02.011 | es_ES |
dc.description.upvformatpinicio | 76 | es_ES |
dc.description.upvformatpfin | 86 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 82 | es_ES |
dc.relation.pasarela | S\246258 | es_ES |
dc.contributor.funder | Universitat de València | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |