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

Detalhes bibliográficos
Autor(a) principal: Melgar-García, Laura
Data de Publicação: 2022
Outros Autores: Gutiérrez-Avilés, David, Godinho, Maria Teresa, Espada, Rita, Brito, Isabel Sofia, Martínez-Álvarez, Francisco, Troncoso, Alicia, Rubio-Escudero, Cristina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/158002
Resumo: Funding 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
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spelling A new big data triclustering approach for extracting three-dimensional patterns in precision agricultureBig data triclusteringPrecision agricultureSpatio-temporal patternsComputer Science ApplicationsCognitive NeuroscienceArtificial IntelligenceFunding 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: © 2022Precision 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.UNINOVA-Instituto de Desenvolvimento de Novas TecnologiasCTS - Centro de Tecnologia e SistemasRUNMelgar-García, LauraGutiérrez-Avilés, DavidGodinho, Maria TeresaEspada, RitaBrito, Isabel SofiaMartínez-Álvarez, FranciscoTroncoso, AliciaRubio-Escudero, Cristina2023-09-19T22:13:43Z2022-08-212022-08-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://hdl.handle.net/10362/158002eng0925-2312PURE: 51575411https://doi.org/10.1016/j.neucom.2021.06.101info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:40:16Zoai:run.unl.pt:10362/158002Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:56:56.628138Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
title A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
spellingShingle A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
Melgar-García, Laura
Big data triclustering
Precision agriculture
Spatio-temporal patterns
Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence
title_short A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
title_full A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
title_fullStr A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
title_full_unstemmed A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
title_sort A new big data triclustering approach for extracting three-dimensional patterns in precision agriculture
author Melgar-García, Laura
author_facet Melgar-García, Laura
Gutiérrez-Avilés, David
Godinho, Maria Teresa
Espada, Rita
Brito, Isabel Sofia
Martínez-Álvarez, Francisco
Troncoso, Alicia
Rubio-Escudero, Cristina
author_role author
author2 Gutiérrez-Avilés, David
Godinho, Maria Teresa
Espada, Rita
Brito, Isabel Sofia
Martínez-Álvarez, Francisco
Troncoso, Alicia
Rubio-Escudero, Cristina
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
CTS - Centro de Tecnologia e Sistemas
RUN
dc.contributor.author.fl_str_mv Melgar-García, Laura
Gutiérrez-Avilés, David
Godinho, Maria Teresa
Espada, Rita
Brito, Isabel Sofia
Martínez-Álvarez, Francisco
Troncoso, Alicia
Rubio-Escudero, Cristina
dc.subject.por.fl_str_mv Big data triclustering
Precision agriculture
Spatio-temporal patterns
Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence
topic Big data triclustering
Precision agriculture
Spatio-temporal patterns
Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence
description Funding 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
publishDate 2022
dc.date.none.fl_str_mv 2022-08-21
2022-08-21T00:00:00Z
2023-09-19T22:13:43Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/158002
url http://hdl.handle.net/10362/158002
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0925-2312
PURE: 51575411
https://doi.org/10.1016/j.neucom.2021.06.101
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11
application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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