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Watermelon Quality Determining from a Photo Using Machine Vision

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dc.contributor.authorVorobyev, G.
dc.contributor.authorSubbotin, D. A.
dc.contributor.authorLosevskaya, E.
dc.date.accessioned2024-11-29T15:28:30Z
dc.date.available2024-11-29T15:28:30Z
dc.date.issued2021
dc.description.abstract© 2021 IEEE.The article discusses methods for determining the quality of watermelon using machine vision, as well as the implementation of these methods in the form of an application. The article describes experiments on using these methods on photographs of real watermelons under various circumstances. To write algorithms, the existing methods for determining the ripeness of a watermelon were studied. In the course of experiments, the problem of implementing these methods using machine vision algorithms was solved. A ready-made solution was obtained that correctly determines the quality of a watermelon from a photograph.
dc.format.extentС. 2286-2289
dc.identifier.citationVorobyev, G. Watermelon Quality Determining from a Photo Using Machine Vision / Vorobyev, G., Subbotin, D.A., Losevskaya, E. // Proceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021. - 2021. - P. 2286-2289. - 10.1109/ElConRus51938.2021.9396492
dc.identifier.doi10.1109/ElConRus51938.2021.9396492
dc.identifier.urihttps://www.doi.org/10.1109/ElConRus51938.2021.9396492
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85104688391&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000669709802068
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/24041
dc.relation.ispartofProceedings of the 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, ElConRus 2021
dc.titleWatermelon Quality Determining from a Photo Using Machine Vision
dc.typeConference Paper
dspace.entity.typePublication
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relation.isOrgUnitOfPublication.latestForDiscovery010157d0-1f75-46b2-ab5b-712e3424b4f5
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