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dc.contributor.authorBlasco, José
dc.contributor.authorAleixos, Nuria
dc.contributor.authorCubero, Sergio
dc.contributor.authorJuste, Florentino
dc.contributor.authorGómez-Sanchís, Juan
dc.contributor.authorAlegre, Vicente
dc.contributor.authorMoltó, Enrique
dc.contributor.editorMoltó, Enrique
dc.date.accessioned2021-11-23T14:53:57Z
dc.date.available2021-11-23T14:53:57Z
dc.date.issued2009es
dc.identifier.citationBlasco, J., Aleixos, N., Cubero, S., Juste, F., Gómez-Sanchis, J., Alegre, V. & Moltó, E. (2009). Computer vision developments for the automatic inspection of fresh and processed fruits. In: Image Analysis for Agricultural Products and Processes-First International Workshop on Computer Image Analysis in Agriculture, 21-34.es
dc.identifier.issn0947-7314
dc.identifier.urihttp://hdl.handle.net/20.500.11939/7764
dc.description.abstractThe quality of a fresh or processed fruit or vegetable is defined by a series of characteristics which make it more or less attractive to the consumer, such as ripeness, size, weight, shape, colour, presence of blemishes and diseases, presence or absence of fruit stems, seeds, etc. In summary, these characteristics may cover all of the factors that exert an influence on the product’s appearance, on its nutritional and organoleptic qualities or on its suitability for preservation. Most of these factors have traditionally been assessed by visual inspection performed by trained operators. However, the application of machine vision in agriculture has increased considerably in recent years since it provides substantial information about the nature and attributes of the produces, reduces costs, guarantees the maintenance of quality standards and provides useful information in real time. Moreover, machine vision opens the possibility of exploring agricultural products in invisible regions of the electromagnetic spectrum, as in the ultraviolet or infrared regions. Instituto Valenciano de Investigaciones Agrarias (IVIA) has developed during the past 15 years computer vision systems for the automatic, on-line inspection of fresh and processed fruits and vegetables. This paper shows the most important outcomes in this matter achieved by the department called Centro de Agroingeniería. One of such systems is a machine for the automatic inspection of pomegranate arils for fresh consumption. This machine individualizes, inspects, classifies and separates the arils in four categories, removing those that do not fulfil the minimal specifications. Multivariate analysis models are used to classify the arils with an average success about 90%. Another application is a machine to classify mandarin segments for canning. The system distinguishes among sound, broken or double segments, and is able to detect the presence of seeds in the segments. The system analyses the shape of the each individual segment to estimate morphological features that are used to classify it into different commercial categories. The machine classifies correctly more than 75% of the analyzed segments. Both systems are currently patent pending. In the field of computer vision systems for the inspection of fresh, whole fruit, most research has been focused on citrus fruits. While most commercial systems only detect the blemishes on the skin of fruit, a multispectral system has been developed to identify them. The system is capable of identifying the 11 most common defects of citrus skin using near infrared, colour and ultraviolet. It also uses induced ultraviolet fluorescence. The success rate achieved with such system reached 87% when identifying about 800 defects in five species of oranges and mandarins. The use of hyperspectral sensors makes it possible to conduct a more sophisticated analysis of the scene by acquiring sets of images corresponding to particular wavelengths. Using this technology, we have conducted different works aimed at detecting damages in citrus fruits, including fungal infestation. The acquired multi-dimensional spectral signature characterising a pixel has been used to analyse scenes and to detect different types of defects such as decay, more easily than using standard colour imaging systems.es
dc.language.isoenes
dc.publisherLeibniz Institute for Agricultural Engineering Potsdam Bornim (ATB)es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectHyperspectral imaginges
dc.titleComputer vision developments for the automatic inspection of fresh and processed fruitses
dc.typeconferenceObjectes
dc.authorAddressblasco_josiva@gva.eses
dc.entidadIVIACentro de Agroingenieríaes
dc.identifier.urlhttp://www2.atb-potsdam.de/CIGR-ImageAnalysis/images/03_061%20Blasco.pdfes
dc.page.final34es
dc.page.initial21es
dc.relation.conferenceDate2009-08-27
dc.relation.conferenceName1st International Workshop on Computer Image Analysis in Agriculturees
dc.relation.conferencePlacePotsdam, Germanyes
dc.relation.projectIDThis work was partially funded by the Spanish Ministry of Science and Technology (MCYT) and European FEDER funds through projects DP12007-66596-C02-02, DPI- 2003-09173-C02-02 and DPI-2007-66596-C02-02, by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) of Spain through project RTA03-105. and by the Instituto Valenciano de Investigaciones Agrarias (IVIA) through the grant “Identificación en tiempo real de los defectos superficiales de los cítricos mediante el empleo de técnicas de computación paralela y visión artificial”.es
dc.rights.accessRightsopenAccesses
dc.source.typeelectronicoes
dc.subject.agrisJ10 Handling, transport, storage and protection of agricultural productses
dc.subject.agrisN01 Agricultural engineeringes
dc.subject.agrisQ01 Food science and technologyes
dc.subject.agrisQ02 Food processing and preservationes
dc.subject.agrovocComputer visiones
dc.subject.agrovocFood qualityes
dc.subject.agrovocPostharvest technologyes
dc.subject.agrovocRipenesses
dc.subject.agrovocSizees
dc.subject.agrovocWeightes
dc.subject.agrovocNutritional valuees
dc.subject.agrovocOrganoleptic qualityes
dc.subject.agrovocFood preservationes
dc.subject.agrovocColores
dc.subject.agrovocPostharvest diseaseses
dc.subject.agrovocFood inspectiones
dc.subject.agrovocFresh fruitses


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