Automatic expert system for weeds/crops identification in images from maize fields

Automation for the identification of plants, based on imaging sensors, in agricultural crops represents an important challenge. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, can be applied for weeds elimination. This requires the identification of wee...

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Detalhes bibliográficos
Autores: Montalvo, Martín, Guerrero, José Miguel, Romeo, Juan, Emmi, Luis Alfredo, Guijarro, María, Pajares, Gonzalo
Formato: artículo
Fecha de publicación:2013
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/413286
Acesso em linha:http://hdl.handle.net/10261/413286
https://api.elsevier.com/content/abstract/scopus_id/84866089740
Access Level:acceso abierto
Palavra-chave:Weeds/crop discrimination
Automatic expert system
Image segmentation
Image thresholding
Maize fields
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spelling Automatic expert system for weeds/crops identification in images from maize fieldsMontalvo, MartínGuerrero, José MiguelRomeo, JuanEmmi, Luis AlfredoGuijarro, MaríaPajares, GonzaloWeeds/crop discriminationAutomatic expert systemImage segmentationImage thresholdingMaize fieldsAutomation for the identification of plants, based on imaging sensors, in agricultural crops represents an important challenge. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, can be applied for weeds elimination. This requires the identification of weeds and crop plants. Sometimes these plants appear impregnated by materials coming from the soil (particularly clays). This appears when the field is irrigated or after rain, particularly when the water falls with some force. This makes traditional approaches based on images greenness identification fail under such situations. Indeed, most pixels belonging to plants, but impregnated, are misidentified as soil pixels because they have lost their natural greenness. This loss of greenness also occurs after treatment when weeds have begun the process of death. To correctly identify all plants, independently of the loss of greenness, we design an automatic expert system based on image segmentation procedures. The performance of this method is verified favorably. © 2012 Elsevier Ltd. All rights reserved.The research leading to these results has been funded by the European Union’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement No. 245986 in the Theme NMP-2009-3.4-1 (Automation and robotics for sustainable crop and forestry management). The authors wish also to acknowledge to the project AGL2011-30442-C02-02, supported by the Ministerio de Economía y Competitividad of Spain within the Plan Nacional de I+D+i.Peer reviewedElsevierEuropean CommissionMinisterio de Economía y Competitividad (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202620262013info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://hdl.handle.net/10261/413286https://api.elsevier.com/content/abstract/scopus_id/84866089740reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/FP7/245986info:eu-repo/grantAgreement/MICINN//AGL2011-30442-C02-02https://doi.org/10.1016/j.eswa.2012.07.034Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4132862026-05-22T06:33:51Z
dc.title.none.fl_str_mv Automatic expert system for weeds/crops identification in images from maize fields
title Automatic expert system for weeds/crops identification in images from maize fields
spellingShingle Automatic expert system for weeds/crops identification in images from maize fields
Montalvo, Martín
Weeds/crop discrimination
Automatic expert system
Image segmentation
Image thresholding
Maize fields
title_short Automatic expert system for weeds/crops identification in images from maize fields
title_full Automatic expert system for weeds/crops identification in images from maize fields
title_fullStr Automatic expert system for weeds/crops identification in images from maize fields
title_full_unstemmed Automatic expert system for weeds/crops identification in images from maize fields
title_sort Automatic expert system for weeds/crops identification in images from maize fields
dc.creator.none.fl_str_mv Montalvo, Martín
Guerrero, José Miguel
Romeo, Juan
Emmi, Luis Alfredo
Guijarro, María
Pajares, Gonzalo
author Montalvo, Martín
author_facet Montalvo, Martín
Guerrero, José Miguel
Romeo, Juan
Emmi, Luis Alfredo
Guijarro, María
Pajares, Gonzalo
author_role author
author2 Guerrero, José Miguel
Romeo, Juan
Emmi, Luis Alfredo
Guijarro, María
Pajares, Gonzalo
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Economía y Competitividad (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Weeds/crop discrimination
Automatic expert system
Image segmentation
Image thresholding
Maize fields
topic Weeds/crop discrimination
Automatic expert system
Image segmentation
Image thresholding
Maize fields
description Automation for the identification of plants, based on imaging sensors, in agricultural crops represents an important challenge. In maize fields, site-specific treatments, with chemical products or mechanical manipulations, can be applied for weeds elimination. This requires the identification of weeds and crop plants. Sometimes these plants appear impregnated by materials coming from the soil (particularly clays). This appears when the field is irrigated or after rain, particularly when the water falls with some force. This makes traditional approaches based on images greenness identification fail under such situations. Indeed, most pixels belonging to plants, but impregnated, are misidentified as soil pixels because they have lost their natural greenness. This loss of greenness also occurs after treatment when weeds have begun the process of death. To correctly identify all plants, independently of the loss of greenness, we design an automatic expert system based on image segmentation procedures. The performance of this method is verified favorably. © 2012 Elsevier Ltd. All rights reserved.
publishDate 2013
dc.date.none.fl_str_mv 2013
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/413286
https://api.elsevier.com/content/abstract/scopus_id/84866089740
url http://hdl.handle.net/10261/413286
https://api.elsevier.com/content/abstract/scopus_id/84866089740
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/FP7/245986
info:eu-repo/grantAgreement/MICINN//AGL2011-30442-C02-02
https://doi.org/10.1016/j.eswa.2012.07.034
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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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