Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops

LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on...

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Detalles Bibliográficos
Autores: Andújar, Dionisio, Escolà, Alexandre, Rosell-Polo, Joan R., Fernández-Quintanilla, César, Dorado, José
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2013
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/242821
Acceso en línea:http://hdl.handle.net/10261/242821
Access Level:acceso abierto
Palabra clave:LiDAR technology
Precision crop protection
Site-specific weed management
Weed discrimination
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spelling Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize cropsAndújar, DionisioEscolà, AlexandreRosell-Polo, Joan R.Fernández-Quintanilla, CésarDorado, JoséLiDAR technologyPrecision crop protectionSite-specific weed managementWeed discriminationLiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on the hypothesis that weed species with different heights can be precisely detected and discriminated using non-contact ranging sensors such as LiDAR. The sensor was placed in the front of an all-terrain vehicle, scanning downwards in a vertical plane, perpendicular to the ground, in order to detect the profile of the vegetation (crop and weeds) above the ground. Measurements were taken on a maize field on 3 m wide (0.45 m2) plots at the time of post-emergence herbicide treatments. Four replications were assessed for each of the four major weed species: Sorghum halepense, Cyperus rotundus, Datura ferox and Xanthium strumarium. The sensor readings were correlated with actual, manually determined, height values (r2 = 0.88). With canonical discriminant analysis the high capabilities of the system to discriminate tall weeds (S. halepense) from shorter ones were quantified. The classification table showed 77.7% of the original grouped cases (i.e., 4800 sampling units) correctly classified for S. halepense. These results indicate that LiDAR sensors are a promising tool for weed detection and discrimination, presenting significant advantages over other types of non-contact ranging sensors such as a higher sampling resolution and its ability to scan at high sampling rates.This research was funded by the Spanish CICyT (Project AGL 2008-04670-C03).Peer reviewedElsevier BVComisión Interministerial de Ciencia y Tecnología, CICYT (España)Andújar, Dionisio [0000-0002-5801-0944]Escolà, Alexandre [0000-0002-9775-5471]Rosell-Polo, Joan R. [0000-0001-6746-2830]Fernández-Quintanilla, César [0000-0002-2886-9176]Dorado, José [0000-0002-2268-2562]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202120212013info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/242821reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1016/j.compag.2012.12.012Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2428212026-05-22T06:33:51Z
dc.title.none.fl_str_mv Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
title Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
spellingShingle Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
Andújar, Dionisio
LiDAR technology
Precision crop protection
Site-specific weed management
Weed discrimination
title_short Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
title_full Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
title_fullStr Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
title_full_unstemmed Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
title_sort Potential of a terrestrial LiDAR-based system to characterise weedvegetation in maize crops
dc.creator.none.fl_str_mv Andújar, Dionisio
Escolà, Alexandre
Rosell-Polo, Joan R.
Fernández-Quintanilla, César
Dorado, José
author Andújar, Dionisio
author_facet Andújar, Dionisio
Escolà, Alexandre
Rosell-Polo, Joan R.
Fernández-Quintanilla, César
Dorado, José
author_role author
author2 Escolà, Alexandre
Rosell-Polo, Joan R.
Fernández-Quintanilla, César
Dorado, José
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Comisión Interministerial de Ciencia y Tecnología, CICYT (España)
Andújar, Dionisio [0000-0002-5801-0944]
Escolà, Alexandre [0000-0002-9775-5471]
Rosell-Polo, Joan R. [0000-0001-6746-2830]
Fernández-Quintanilla, César [0000-0002-2886-9176]
Dorado, José [0000-0002-2268-2562]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv LiDAR technology
Precision crop protection
Site-specific weed management
Weed discrimination
topic LiDAR technology
Precision crop protection
Site-specific weed management
Weed discrimination
description LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on the hypothesis that weed species with different heights can be precisely detected and discriminated using non-contact ranging sensors such as LiDAR. The sensor was placed in the front of an all-terrain vehicle, scanning downwards in a vertical plane, perpendicular to the ground, in order to detect the profile of the vegetation (crop and weeds) above the ground. Measurements were taken on a maize field on 3 m wide (0.45 m2) plots at the time of post-emergence herbicide treatments. Four replications were assessed for each of the four major weed species: Sorghum halepense, Cyperus rotundus, Datura ferox and Xanthium strumarium. The sensor readings were correlated with actual, manually determined, height values (r2 = 0.88). With canonical discriminant analysis the high capabilities of the system to discriminate tall weeds (S. halepense) from shorter ones were quantified. The classification table showed 77.7% of the original grouped cases (i.e., 4800 sampling units) correctly classified for S. halepense. These results indicate that LiDAR sensors are a promising tool for weed detection and discrimination, presenting significant advantages over other types of non-contact ranging sensors such as a higher sampling resolution and its ability to scan at high sampling rates.
publishDate 2013
dc.date.none.fl_str_mv 2013
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/242821
url http://hdl.handle.net/10261/242821
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1016/j.compag.2012.12.012
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier BV
publisher.none.fl_str_mv Elsevier BV
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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