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dc.contributor.authorBlasco, José
dc.contributor.authorMunera, Sandra
dc.contributor.authorAleixos, Nuria
dc.contributor.authorCubero, Sergio
dc.contributor.authorMoltó, Enrique
dc.contributor.editorHitzmann,B.
dc.date.accessioned2018-05-09T16:30:56Z
dc.date.available2018-05-09T16:30:56Z
dc.date.issued2017
dc.identifier.citationBlasco, J., Munera, S., Aleixos, N., Cubero, S., Molto, E. (2017). Machine vision-based measurement systems for fruit and vegetable quality control in postharvest. Measurement, Modeling and Automation in Advanced Food Processing, 161, 71-91.
dc.identifier.issn0724-6145
dc.identifier.urihttp://hdl.handle.net/20.500.11939/6027
dc.description.abstractIndividual items of any agricultural commodity are different from each other in terms of colour, shape or size. Furthermore, as they are living thing, they change their quality attributes over time, thereby making the development of accurate automatic inspection machines a challenging task. Machine vision-based systems and new optical technologies make it feasible to create non-destructive control and monitoring tools for quality assessment to ensure adequate accomplishment of food standards. Such systems are much faster than any manual non-destructive examination of fruit and vegetable quality, thus allowing the whole production to be inspected with objective and repeatable criteria. Moreover, current technology makes it possible to inspect the fruit in spectral ranges beyond the sensibility of the human eye, for instance in the ultraviolet and near-infrared regions. Machine vision-based applications require the use of multiple technologies and knowledge, ranging from those related to image acquisition (illumination, cameras, etc.) to the development of algorithms for spectral image analysis. Machine vision-based systems for inspecting fruit and vegetables are targeted towards different purposes, from in-line sorting into commercial categories to the detection of contaminants or the distribution of specific chemical compounds on the product's surface. This chapter summarises the current state of the art in these techniques, starting with systems based on colour images for the inspection of conventional colour, shape or external defects and then goes on to consider recent developments in spectral image analysis for internal quality assessment or contaminant detection.
dc.language.isoen
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleMachine Vision-Based Measurement Systems for Fruit and Vegetable Quality Control in Postharvest
dc.typearticle
dc.entidadIVIACentro de Agroingeniería
dc.identifier.doi10.1007/10_2016_51
dc.identifier.url
dc.journal.abbreviatedTitle
dc.journal.issueNumber
dc.journal.titleMeasurement, Modeling and Automation in Advanced Food Processing
dc.journal.volumeNumber161
dc.page.final91
dc.page.initial71
dc.source.typeelectronico


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Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España