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A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture

2022-08-21Artigo científico Acesso aberto
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dc.contributor.authorMelgar-García, Laura
dc.contributor.authorGutiérrez-Avilés, David
dc.contributor.authorGodinho, Maria Teresa
dc.contributor.authorEspada, Rita
dc.contributor.authorBrito, Isabel Sofia
dc.contributor.authorMartínez-Álvarez, Francisco
dc.contributor.authorTroncoso, Alicia
dc.contributor.authorRubio-Escudero, Cristina
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.pblElsevier Science B.V., Amsterdam.
dc.date.accessioned2023-09-19T22:13:43Z
dc.date.available2023-09-19T22:13:43Z
dc.date.issued2022-08-21
dc.descriptionFunding Information: The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the project PID2020-117954RB and the European Regional Development Fund and Junta de Andalucía for projects PY20-00870 and UPO-138516. This work could not have been done without the support and help of the Farmer’s Association of Baixo Alentejo and Francisco Palma during the whole project. Finally, the authors thank António Vieira Lima and Moragri S. A. for giving access to data. Publisher Copyright: © 2022
dc.description.abstractPrecision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional patterns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent1779465
dc.identifier.doi10.1016/j.neucom.2021.06.101
dc.identifier.issn0925-2312
dc.identifier.otherPURE: 51575411
dc.identifier.otherPURE UUID: 1b012e62-3c4e-4688-bb54-648cbc69b6f4
dc.identifier.otherScopus: 85131097736
dc.identifier.otherWOS: 000809656200008
dc.identifier.urihttp://hdl.handle.net/10362/158002
dc.identifier.urlhttps://www.scopus.com/pages/publications/85131097736
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
dc.relationCentre of Technology and Systems
dc.subjectBig data triclustering
dc.subjectPrecision agriculture
dc.subjectSpatio-temporal patterns
dc.subjectComputer Science Applications
dc.subjectCognitive Neuroscience
dc.subjectArtificial Intelligence
dc.titleA new big data triclustering approach for extracting three-dimensional patterns in precision agricultureen
dc.typejournal article
degois.publication.firstPage268
degois.publication.lastPage278
degois.publication.titleNeurocomputing
degois.publication.volume500
dspace.entity.typePublication
oaire.awardNumberUIDB/00066/2020
oaire.awardTitleCentre of Technology and Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication779ae807-14a4-4e58-87e2-6dd81eb7a030
relation.isProjectOfPublication.latestForDiscovery779ae807-14a4-4e58-87e2-6dd81eb7a030

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