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dc.creatorMelgar García, Lauraes
dc.creatorGodinho, María Teresaes
dc.creatorEspada, Ritaes
dc.creatorGutiérrez Avilés, Davides
dc.creatorBrito, Isabel Sofíaes
dc.creatorMartínez Álvarez, Franciscoes
dc.creatorTroncoso Lora, Aliciaes
dc.creatorRubio Escudero, Cristinaes
dc.date.accessioned2022-04-04T09:00:43Z
dc.date.available2022-04-04T09:00:43Z
dc.date.issued2020
dc.identifier.citationMelgar García, L., Godinho, M.T., Espada, R., Gutiérrez Avilés, D., Brito, I.S., Martínez Álvarez, F.,...,Rubio Escudero, C. (2020). Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering. En SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (226-236), Burgos, España: Springer.
dc.identifier.isbn978-3-030-57801-5es
dc.identifier.urihttps://hdl.handle.net/11441/131704
dc.description.abstractAgriculture has undergone some very important changes over the last few decades. The emergence and evolution of precision agri culture has allowed to move from the uniform site management to the site-specific management, with both economic and environmental advan tages. However, to be implemented effectively, site-specific management requires within-field spatial variability to be well-known and character ized. In this paper, an algorithm that delineates within-field management zones in a maize plantation is introduced. The algorithm, based on tri clustering, mines clusters from temporal remote sensing data. Data from maize crops in Alentejo, Portugal, have been used to assess the suit ability of applying triclustering to discover patterns over time, that may eventually help farmers to improve their harvests.es
dc.description.sponsorshipMinisterio de Economía y Competitividad TIN2017-88209-C2es
dc.description.sponsorshipFundaçao para a Ciéncia e a Tecnologia (FCT) UIDB/04561/2020es
dc.formatapplication/pdfes
dc.format.extent11es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofSOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (2020), pp. 226-236.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTriclusteringes
dc.subjectSpatio-temporal patternses
dc.subjectPrecision agriculturees
dc.subjectRemote sensinges
dc.titleDiscovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclusteringes
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDTIN2017-88209-C2es
dc.relation.projectIDUIDB/04561/2020es
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-030-57802-2_22es
dc.identifier.doi10.1007/978-3-030-57802-2_22es
dc.publication.initialPage226es
dc.publication.endPage236es
dc.eventtitleSOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applicationses
dc.eventinstitutionBurgos, Españaes
dc.relation.publicationplaceCham, Switzerlandes
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). Españaes
dc.contributor.funderFundaçao para a Ciencia e a Tecnología (FCT)es

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