dc.creator | Melgar García, Laura | es |
dc.creator | Godinho, María Teresa | es |
dc.creator | Espada, Rita | es |
dc.creator | Gutiérrez Avilés, David | 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 | 2022-04-04T09:00:43Z | |
dc.date.available | 2022-04-04T09:00:43Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Melgar 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.isbn | 978-3-030-57801-5 | es |
dc.identifier.uri | https://hdl.handle.net/11441/131704 | |
dc.description.abstract | Agriculture 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.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2 | es |
dc.description.sponsorship | Fundaçao para a Ciéncia e a Tecnologia (FCT) UIDB/04561/2020 | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (2020), pp. 226-236. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Triclustering | es |
dc.subject | Spatio-temporal patterns | es |
dc.subject | Precision agriculture | es |
dc.subject | Remote sensing | es |
dc.title | Discovering Spatio-Temporal Patterns in Precision Agriculture Based on Triclustering | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
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 | TIN2017-88209-C2 | es |
dc.relation.projectID | UIDB/04561/2020 | es |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-57802-2_22 | es |
dc.identifier.doi | 10.1007/978-3-030-57802-2_22 | es |
dc.publication.initialPage | 226 | es |
dc.publication.endPage | 236 | es |
dc.eventtitle | SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications | es |
dc.eventinstitution | Burgos, España | es |
dc.relation.publicationplace | Cham, Switzerland | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Fundaçao para a Ciencia e a Tecnología (FCT) | es |