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dc.contributor.authorCubero, Sergio
dc.contributor.authorAleixos, Nuria
dc.contributor.authorAlbert, Francisco
dc.contributor.authorTorregrosa, Antonio
dc.contributor.authorOrtiz, Coral
dc.contributor.authorGarcía-Navarrete, Óscar L.
dc.contributor.authorBlasco, José
dc.date.accessioned2017-06-01T10:11:37Z
dc.date.available2017-06-01T10:11:37Z
dc.date.issued2014
dc.identifier.citationCubero, S., Aleixos, N., Albert, F., Torregrosa, A., Ortiz, C., Garcia-Navarrete, O., Blasco, J. (2014). Optimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform. Precision Agriculture, 15(1), 80-94.
dc.identifier.issn1385-2256
dc.identifier.urihttp://hdl.handle.net/20.500.11939/5059
dc.description.abstractThe mechanisation and automation of citrus harvesting is considered to be one of the best options to reduce production costs. Computer vision technology has been shown to be a useful tool for fresh fruit and vegetable inspection, and is currently used in post-harvest fruit and vegetable automated grading systems in packing houses. Although computer vision technology has been used in some harvesting robots, it is not commonly utilised in fruit grading during harvesting due to the difficulties involved in adapting it to field conditions. Carrying out fruit inspection before arrival at the packing lines could offer many advantages, such as having an accurate fruit assessment in order to decide among different fruit treatments or savings in the cost of transport and marketing non-commercial fruit. This work presents a computer vision system, mounted on a mobile platform where workers place the harvested fruits, that was specially designed for sorting fruit in the field. Due to the specific field conditions, an efficient and robust lighting system, very low-power image acquisition and processing hardware, and a reduced inspection chamber had to be developed. The equipment is capable of analysing fruit colour and size at a speed of eight fruits per second. The algorithms developed achieved prediction accuracy with an R-2 coefficient of 0.993 for size estimation and an R-2 coefficient of 0.918 for the colour index.
dc.language.isoen
dc.titleOptimised computer vision system for automatic pre-grading of citrus fruit in the field using a mobile platform
dc.typearticle
dc.authorAddressInstituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315, Km. 10’7, 46113 Moncada (Valencia), Españaes
dc.date.issuedFreeFormFEB 2014
dc.entidadIVIACentro de Agroingeniería
dc.identifier.doi10.1007/s11119-013-9324-7
dc.journal.issueNumber1
dc.journal.titlePrecision Agriculture
dc.journal.volumeNumber15
dc.page.final94
dc.page.initial80
dc.relation.projectIDThis research work has been funded by the Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria de España (INIA) and the European FEDER funds (projects RTA2009- 00118-C02-01 and RTA2009-00118-C02-02).
dc.rights.accessRightsopenAccess
dc.source.typeImpreso
dc.type.hasVersionacceptedVersion


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