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dc.contributor.authorLópez-García, Fernando
dc.contributor.authorAndreu-Garcia, Gabriela
dc.contributor.authorBlasco, José
dc.contributor.authorAleixos, Nuria
dc.contributor.authorValiente, José Miguel
dc.date.accessioned2017-06-01T10:12:31Z
dc.date.available2017-06-01T10:12:31Z
dc.date.issued2010
dc.identifier.citationLopez-Garcia, F., Andreu-Garcia, Gabriela, Blasco, J., Aleixos, N., Valiente, J.M. (2010). Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Computers and Electronics in Agriculture, 71(2), 189-197.
dc.identifier.issn0168-1699
dc.identifier.urihttp://hdl.handle.net/20.500.11939/5531
dc.description.abstractOne of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T(2) statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol. Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection. (C) 2010 Elsevier B.V. All rights reserved.
dc.language.isoen
dc.titleAutomatic detection of skin defects in citrus fruits using a multivariate image analysis approach
dc.typearticle
dc.authorAddressInstituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315, Km. 10’7, 46113 Moncada (Valencia), Españaes
dc.date.issuedFreeFormMAY 2010
dc.entidadIVIACentro de Agroingeniería
dc.identifier.doi10.1016/j.compag.2010.02.001
dc.journal.abbreviatedTitleComput.Electron.Agric.
dc.journal.issueNumber2
dc.journal.titleComputers and Electronics in Agriculture
dc.journal.volumeNumber71
dc.page.final197
dc.page.initial189
dc.rights.accessRightsopenAccess
dc.source.typeImpreso
dc.type.hasVersionacceptedVersion


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