dc.creator | Melgar García, Laura | es |
dc.creator | Gutiérrez Avilés, David | es |
dc.creator | Godinho, María Teresa | es |
dc.creator | Espada, Rita | es |
dc.creator | Brito, Isabel Sofía | es |
dc.creator | Martínez Álvarez, Francisco | es |
dc.creator | Troncoso Lora, Alicia | es |
dc.creator | Rubio Escudero, Cristina | es |
dc.date.accessioned | 2022-12-01T12:42:19Z | |
dc.date.available | 2022-12-01T12:42:19Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Melgar García, L., Gutiérrez Avilés, D., Godinho, M.T., Espada, R., Brito, I.S., Martínez Álvarez, F.,...,Rubio Escudero, C. (2022). A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture. Neurocomputing, 500 (August 2022), 268-278. https://doi.org/10.1016/j.neucom.2021.06.101. | |
dc.identifier.issn | 0925-2312 | es |
dc.identifier.issn | 1872-8286 | es |
dc.identifier.uri | https://hdl.handle.net/11441/140020 | |
dc.description.abstract | Precision 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 pat-terns 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. | es |
dc.description.sponsorship | Ministerio de Ciencia e Innovación PID2020-117954RB | es |
dc.description.sponsorship | Junta de Andalucía PY20-00870 | es |
dc.description.sponsorship | Junta de Andalucía UPO-138516 | es |
dc.description.sponsorship | Fundação para a Ciência e a Tecnologia (FCT) UIDB/00066/2020 | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Neurocomputing, 500 (August 2022), 268-278. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Big data triclustering | es |
dc.subject | Precision agriculture | es |
dc.subject | Spatio-temporal patterns | es |
dc.title | A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | PID2020-117954RB | es |
dc.relation.projectID | PY20-00870 | es |
dc.relation.projectID | UPO-138516 | es |
dc.relation.projectID | UIDB/00066/2020 | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0925231222006415?via%3Dihub | es |
dc.identifier.doi | 10.1016/j.neucom.2021.06.101 | es |
dc.contributor.group | Universidad de Sevilla. TIC-254: Data Science and Big Data Lab | es |
dc.contributor.group | Universidad de Sevilla. TIC-134: Sistemas Informáticos | es |
dc.journaltitle | Neurocomputing | es |
dc.publication.volumen | 500 | es |
dc.publication.issue | August 2022 | es |
dc.publication.initialPage | 268 | es |
dc.publication.endPage | 278 | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (MICIN). España | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.contributor.funder | Fundação para a Ciência e a Tecnologia (FCT) | es |