Show simple item record

dc.contributor.authorCalvario, Gabriela
dc.contributor.authorSierra Araujo, Basilio
dc.contributor.authorAlarcón, Teresa E.
dc.contributor.authorHernández Gómez, María del Carmen
dc.contributor.authorDalmau, Oscar
dc.date.accessioned2018-04-09T08:50:45Z
dc.date.available2018-04-09T08:50:45Z
dc.date.issued2017-06
dc.identifier.citationSensors 17(6) : (2017) // Article ID 1411es_ES
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10810/26169
dc.description.abstractThe use of Unmanned Aerial Vehicles (UAVs) based on remote sensing has generated low cost monitoring, since the data can be acquired quickly and easily. This paper reports the experience related to agave crop analysis with a low cost UAV. The data were processed by traditional photogrammetric flow and data extraction techniques were applied to extract new layers and separate the agave plants from weeds and other elements of the environment. Our proposal combines elements of photogrammetry, computer vision, data mining, geomatics and computer science. This fusion leads to very interesting results in agave control. This paper aims to demonstrate the potential of UAV monitoring in agave crops and the importance of information processing with reliable data flow.es_ES
dc.description.sponsorshipWe wish to acknowledge the Consejo Nacional de Ciencia y Tecnologia (CONACYT) for its financial support to the PhD studies of Gabriela Calvario. We are grateful to Cubo Geoespacial S.A .de C.V. and special to Ing. Jordan Martinez for the stimulus to this work, more information about this Company is available at: http://www.cubogeoespacial.com/. In addition, we are grateful to the support of the Tequila Regulatory Council (CRT), which has allowed us to monitor several crops. This paper has been supported by the Spanish Ministerio de Economia y Competitividad, contract TIN2015-64395-R (MINECO/FEDER, UE), as well as by the Basque Government, contract IT900-16. This work was also supported in part by CONACYT (Mexico), Grant 258033.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/TIN2015-64395-Res_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectUAVes_ES
dc.subjectdata mininges_ES
dc.subjectcomputer visiones_ES
dc.subjectgeomaticses_ES
dc.subjectagave monitoringes_ES
dc.subjectsupport vector machineses_ES
dc.subjectclassificationes_ES
dc.subjectvegetationes_ES
dc.subjectagriculturees_ES
dc.subjectmanagementes_ES
dc.subjectimageses_ES
dc.subjectredes_ES
dc.titleA Multi-Disciplinary Approach to Remote Sensing through Low-Cost UAVses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holder© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttp://www.mdpi.com/1424-8220/17/6/1411es_ES
dc.identifier.doi10.3390/s17061411
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).