Learning to detect vegetation using computer vision and lowcost cameras

Autores
Felissia, Sergio; Redolfi, Javier; Benardi, Emanuel; Araguás , Roberto Gastón; Felisa, Ana Georgina
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A problem of current agriculture is the large amount of agrochemicals used to boost production due to their cost and the environmental pollution they cause. A partial solution to this problem consists in developing selective spraying techniques through the measurement of a green index that allows the selection of the precise amount of pesticide to be applied according to the specific conditions of each part of the field. Some of the problems of the existing systems are the inability to discriminate between types of vegetation and to pinpoint its location, since they only detect general patches of vegetation. In this work, we introduce a system prototype capable of measuring the presence of vegetation in an area using lowcost devices combined with current computer vision techniques. The system allows to generate a mask with the presence of vegetation in a certain area and it is also capable of distinguishing between different materials unlike current methods, which only allow to distinguish between green and non-green areas. The presented method opens the door to future research which can allow distinguishing between crops and weeds to make an even more selective application. The output of the system can be used also to design another type of weeding method that is not based on the application of agrochemicals.
Fil: Felissia, Sergio. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Redolfi, Javier. Universidad Tecnológica Nacional. Facultad Regional San Francisco. Grupo de Investigación Sobre Aplicaciones Inteligentes; Argentina.
Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Araguás, Roberto Gastón. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Peer Reviewed
Materia
Detect vegetation
Agriculture
Low cost cameras
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Attribution-NonCommercial-NoDerivatives 4.0 International
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/14496
Acceder
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network_acronym_str RIAUTN
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network_name_str Repositorio Institucional Abierto (UTN)
spelling Learning to detect vegetation using computer vision and lowcost camerasFelissia, SergioRedolfi, JavierBenardi, EmanuelAraguás , Roberto GastónFelisa, Ana GeorginaDetect vegetationAgricultureLow cost camerasA problem of current agriculture is the large amount of agrochemicals used to boost production due to their cost and the environmental pollution they cause. A partial solution to this problem consists in developing selective spraying techniques through the measurement of a green index that allows the selection of the precise amount of pesticide to be applied according to the specific conditions of each part of the field. Some of the problems of the existing systems are the inability to discriminate between types of vegetation and to pinpoint its location, since they only detect general patches of vegetation. In this work, we introduce a system prototype capable of measuring the presence of vegetation in an area using lowcost devices combined with current computer vision techniques. The system allows to generate a mask with the presence of vegetation in a certain area and it is also capable of distinguishing between different materials unlike current methods, which only allow to distinguish between green and non-green areas. The presented method opens the door to future research which can allow distinguishing between crops and weeds to make an even more selective application. The output of the system can be used also to design another type of weeding method that is not based on the application of agrochemicals.Fil: Felissia, Sergio. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.Fil: Redolfi, Javier. Universidad Tecnológica Nacional. Facultad Regional San Francisco. Grupo de Investigación Sobre Aplicaciones Inteligentes; Argentina.Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.Fil: Araguás, Roberto Gastón. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Peer ReviewedIEEE2026-02-12T19:38:40Z2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfIEEE International Conference on Industrial Technology, 2020.https://hdl.handle.net/20.500.12272/14496enginfo:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Felissia, Sergio; Redolfi, Javier; Bernardi, Emanuel; Aragúas, Roberto Gastón; Flesia, Ana Georgina.https://creativecommons.org/licenses/by-nc-nd/4.0/reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:46:51Zoai:ria.utn.edu.ar:20.500.12272/14496instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:46:53.567Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
dc.title.none.fl_str_mv Learning to detect vegetation using computer vision and lowcost cameras
title Learning to detect vegetation using computer vision and lowcost cameras
spellingShingle Learning to detect vegetation using computer vision and lowcost cameras
Felissia, Sergio
Detect vegetation
Agriculture
Low cost cameras
title_short Learning to detect vegetation using computer vision and lowcost cameras
title_full Learning to detect vegetation using computer vision and lowcost cameras
title_fullStr Learning to detect vegetation using computer vision and lowcost cameras
title_full_unstemmed Learning to detect vegetation using computer vision and lowcost cameras
title_sort Learning to detect vegetation using computer vision and lowcost cameras
dc.creator.none.fl_str_mv Felissia, Sergio
Redolfi, Javier
Benardi, Emanuel
Araguás , Roberto Gastón
Felisa, Ana Georgina
author Felissia, Sergio
author_facet Felissia, Sergio
Redolfi, Javier
Benardi, Emanuel
Araguás , Roberto Gastón
Felisa, Ana Georgina
author_role author
author2 Redolfi, Javier
Benardi, Emanuel
Araguás , Roberto Gastón
Felisa, Ana Georgina
author2_role author
author
author
author
dc.subject.none.fl_str_mv Detect vegetation
Agriculture
Low cost cameras
topic Detect vegetation
Agriculture
Low cost cameras
dc.description.none.fl_txt_mv A problem of current agriculture is the large amount of agrochemicals used to boost production due to their cost and the environmental pollution they cause. A partial solution to this problem consists in developing selective spraying techniques through the measurement of a green index that allows the selection of the precise amount of pesticide to be applied according to the specific conditions of each part of the field. Some of the problems of the existing systems are the inability to discriminate between types of vegetation and to pinpoint its location, since they only detect general patches of vegetation. In this work, we introduce a system prototype capable of measuring the presence of vegetation in an area using lowcost devices combined with current computer vision techniques. The system allows to generate a mask with the presence of vegetation in a certain area and it is also capable of distinguishing between different materials unlike current methods, which only allow to distinguish between green and non-green areas. The presented method opens the door to future research which can allow distinguishing between crops and weeds to make an even more selective application. The output of the system can be used also to design another type of weeding method that is not based on the application of agrochemicals.
Fil: Felissia, Sergio. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Redolfi, Javier. Universidad Tecnológica Nacional. Facultad Regional San Francisco. Grupo de Investigación Sobre Aplicaciones Inteligentes; Argentina.
Fil: Bernardi, Emanuel. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Araguás, Roberto Gastón. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería; Argentina.
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Peer Reviewed
description A problem of current agriculture is the large amount of agrochemicals used to boost production due to their cost and the environmental pollution they cause. A partial solution to this problem consists in developing selective spraying techniques through the measurement of a green index that allows the selection of the precise amount of pesticide to be applied according to the specific conditions of each part of the field. Some of the problems of the existing systems are the inability to discriminate between types of vegetation and to pinpoint its location, since they only detect general patches of vegetation. In this work, we introduce a system prototype capable of measuring the presence of vegetation in an area using lowcost devices combined with current computer vision techniques. The system allows to generate a mask with the presence of vegetation in a certain area and it is also capable of distinguishing between different materials unlike current methods, which only allow to distinguish between green and non-green areas. The presented method opens the door to future research which can allow distinguishing between crops and weeds to make an even more selective application. The output of the system can be used also to design another type of weeding method that is not based on the application of agrochemicals.
publishDate 2020
dc.date.none.fl_str_mv 2020
2026-02-12T19:38:40Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv IEEE International Conference on Industrial Technology, 2020.
https://hdl.handle.net/20.500.12272/14496
identifier_str_mv IEEE International Conference on Industrial Technology, 2020.
url https://hdl.handle.net/20.500.12272/14496
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Felissia, Sergio; Redolfi, Javier; Bernardi, Emanuel; Aragúas, Roberto Gastón; Flesia, Ana Georgina.
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
Felissia, Sergio; Redolfi, Javier; Bernardi, Emanuel; Aragúas, Roberto Gastón; Flesia, Ana Georgina.
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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