Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/29910
DC FieldValueLanguage
dc.contributor.authorKaburlasos, Vassilis G.-
dc.contributor.authorVrochidou, Eleni-
dc.contributor.authorLytridis, Chris-
dc.contributor.authorPapakostas, George A.-
dc.contributor.authorPachidis, Theodore P.-
dc.contributor.authorManios, Michail-
dc.contributor.authorMamalis, Spyridon A.-
dc.contributor.authorMerou, Theodora P.-
dc.contributor.authorKoundouras, Stefanos-
dc.contributor.authorTheocharis, Serafeim-
dc.contributor.authorSiavalas, George-
dc.contributor.authorSgouros, Christos-
dc.contributor.authorKyriakidis, Phaedon-
dc.date.accessioned2023-07-20T06:35:50Z-
dc.date.available2023-07-20T06:35:50Z-
dc.date.issued2020-07-01-
dc.identifier.citationInternational Joint Conference on Neural Networks, IJCNN 2020Virtual, Glasgow, 19 - 24 July 2020en_US
dc.identifier.isbn9781728169262-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/29910-
dc.description.abstractThe automation of agricultural production calls for accurate prediction of the harvest time. Our interest in particular here is in grape harvest time. Nevertheless, the latter prediction is not trivial also due to the scale of data involved. We propose a novel neural network architecture that processes whole histograms induced from digital images. A histogram is represented by an Intervals' Number (IN); hence, all-order data statistics are represented. In conclusion, the proposed IN Neural Network, or INNN for short, emerges with the capacity of predicting an IN from past INs. We demonstrate a proof-of-concept, preliminary application on a time series of digital images of grapes taken during their growth to maturity. Compared to a conventional Back Propagation Neural Network (BPNN), the results by INNN are superior not only in terms of prediction accuracy but also because the BPNN predicts only first-order data statistics, whereas the INNN predicts all-order data statistics.en_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAutonomous Roboten_US
dc.subjectBig Dataen_US
dc.subjectDexterous Farmingen_US
dc.subjectGrape Harvesten_US
dc.subjectNeural Computingen_US
dc.subjectPrediction Modelen_US
dc.titleToward Big Data Manipulation for Grape Harvest Time Prediction by Intervals' Numbers Techniquesen_US
dc.typeConference Posteren_US
dc.collaborationInternational Hellenic Universityen_US
dc.collaborationEuroactionen_US
dc.collaborationKtima Pavlidisen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsSubscriptionen_US
dc.countryGreeceen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.conferenceProceedings of the International Joint Conference on Neural Networksen_US
dc.identifier.doi10.1109/IJCNN48605.2020.9206965en_US
dc.identifier.scopus2-s2.0-85093842247-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85093842247-
cut.common.academicyear2019-2020en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Poster-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0003-4222-8567-
crisitem.author.parentorgFaculty of Engineering and Technology-
Appears in Collections:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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