Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage
- Autores
- Bussi, Ulises; Sauczuk, Martín; Mandile, Guillermo; Poggio, Santiago; Oliva, Damián
- Año de publicación
- 2023
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- The detection, geolocation, and classification of weeds in agricultural fields is a problem of interest associated with Precision Agriculture (PA). The main contribution of this work is to describe a workflow (feasible to automate) based on open-source software tools and open information to: 1) measure the spatiotemporal evolution of weed patches through satellite images, and 2) register high-resolution images (taken at low altitude) on top of the satellite image to identify the weeds that compose the detected patches. To merge the satellite and low-altitude information, the following problems must be solved: 1) correct distortions in the acquired images; 2) develop an image formation model that allows registering the low-altitude image on top of the satellite image, and 3) analyze green indices to measure patch coverage in both multiespectral satellite images and RGB images obtained from a camera mounted on an unmanned aerial vehicle. Finally, the feasibility of merging information is demonstrated through an analysis of the correlation in the coverage measures obtained from satellite and low-altitude images.
La deteccion, geolocalizacion y clasificacion de malezas en campos agricolas es un problema de interes asociado a la Agricultura de Precision (AP). El aporte principal de este trabajo es describir un flujo de trabajo (factible de automatizar) basado en herramientas de software libre e informacion abierta para: 1) medir la evolucion espaciotemporal de los parches de malezas a traves de imagenes satelitales y; 2) registrar las imagenes de alta resolucion (tomadas a baja altura) sobre la imagen satelital, para identificar las malezas que componen los parches detectados. Para fusionar la informacion satelital y de baja altura, se deben resolver los siguientes problemas: 1) corregir las distorsiones en las imagenes adquiridas; 2) desarrollar un modelo de formacion de imagenes que permita registrar la imagen a baja altura sobre la imagen satelital; 3) analizar los indices de verde para medir la cobertura de los parches, tanto en las imagenes satelitales multiespectrales, como en las imagenes RGB obtenidas desde una camara montada en un vehıculo aereo no tripulado. Finalmente, se muestra la factibilidad de realizar la fusion de informacion a partir de un analisis de la correlacion en las medidas de cobertura obtenidas de las imagenes satelitales y de las de baja altura.
Sociedad Argentina de Informática e Investigación Operativa - Materia
-
Ciencias Informáticas
Weed
Sentinel-2
Unmanned Aerial Vehicle
UAV
Malezas - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc/4.0/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/157807
Ver los metadatos del registro completo
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Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverageFusión de información satelital con vuelos a baja altura de vehículos aéreos no tripulados para estimar la cobertura de malezasBussi, UlisesSauczuk, MartínMandile, GuillermoPoggio, SantiagoOliva, DamiánCiencias InformáticasWeedSentinel-2Unmanned Aerial VehicleUAVMalezasThe detection, geolocation, and classification of weeds in agricultural fields is a problem of interest associated with Precision Agriculture (PA). The main contribution of this work is to describe a workflow (feasible to automate) based on open-source software tools and open information to: 1) measure the spatiotemporal evolution of weed patches through satellite images, and 2) register high-resolution images (taken at low altitude) on top of the satellite image to identify the weeds that compose the detected patches. To merge the satellite and low-altitude information, the following problems must be solved: 1) correct distortions in the acquired images; 2) develop an image formation model that allows registering the low-altitude image on top of the satellite image, and 3) analyze green indices to measure patch coverage in both multiespectral satellite images and RGB images obtained from a camera mounted on an unmanned aerial vehicle. Finally, the feasibility of merging information is demonstrated through an analysis of the correlation in the coverage measures obtained from satellite and low-altitude images.La deteccion, geolocalizacion y clasificacion de malezas en campos agricolas es un problema de interes asociado a la Agricultura de Precision (AP). El aporte principal de este trabajo es describir un flujo de trabajo (factible de automatizar) basado en herramientas de software libre e informacion abierta para: 1) medir la evolucion espaciotemporal de los parches de malezas a traves de imagenes satelitales y; 2) registrar las imagenes de alta resolucion (tomadas a baja altura) sobre la imagen satelital, para identificar las malezas que componen los parches detectados. Para fusionar la informacion satelital y de baja altura, se deben resolver los siguientes problemas: 1) corregir las distorsiones en las imagenes adquiridas; 2) desarrollar un modelo de formacion de imagenes que permita registrar la imagen a baja altura sobre la imagen satelital; 3) analizar los indices de verde para medir la cobertura de los parches, tanto en las imagenes satelitales multiespectrales, como en las imagenes RGB obtenidas desde una camara montada en un vehıculo aereo no tripulado. Finalmente, se muestra la factibilidad de realizar la fusion de informacion a partir de un analisis de la correlacion en las medidas de cobertura obtenidas de las imagenes satelitales y de las de baja altura.Sociedad Argentina de Informática e Investigación Operativa2023-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf2-21http://sedici.unlp.edu.ar/handle/10915/157807enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/EJS/article/view/495info:eu-repo/semantics/altIdentifier/issn/1514-6774info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:13:06Zoai:sedici.unlp.edu.ar:10915/157807Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:13:06.263SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage Fusión de información satelital con vuelos a baja altura de vehículos aéreos no tripulados para estimar la cobertura de malezas |
title |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage |
spellingShingle |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage Bussi, Ulises Ciencias Informáticas Weed Sentinel-2 Unmanned Aerial Vehicle UAV Malezas |
title_short |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage |
title_full |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage |
title_fullStr |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage |
title_full_unstemmed |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage |
title_sort |
Satellite information fusion with low-altitude unmanned aerial vehicle flights for estimating weed coverage |
dc.creator.none.fl_str_mv |
Bussi, Ulises Sauczuk, Martín Mandile, Guillermo Poggio, Santiago Oliva, Damián |
author |
Bussi, Ulises |
author_facet |
Bussi, Ulises Sauczuk, Martín Mandile, Guillermo Poggio, Santiago Oliva, Damián |
author_role |
author |
author2 |
Sauczuk, Martín Mandile, Guillermo Poggio, Santiago Oliva, Damián |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Weed Sentinel-2 Unmanned Aerial Vehicle UAV Malezas |
topic |
Ciencias Informáticas Weed Sentinel-2 Unmanned Aerial Vehicle UAV Malezas |
dc.description.none.fl_txt_mv |
The detection, geolocation, and classification of weeds in agricultural fields is a problem of interest associated with Precision Agriculture (PA). The main contribution of this work is to describe a workflow (feasible to automate) based on open-source software tools and open information to: 1) measure the spatiotemporal evolution of weed patches through satellite images, and 2) register high-resolution images (taken at low altitude) on top of the satellite image to identify the weeds that compose the detected patches. To merge the satellite and low-altitude information, the following problems must be solved: 1) correct distortions in the acquired images; 2) develop an image formation model that allows registering the low-altitude image on top of the satellite image, and 3) analyze green indices to measure patch coverage in both multiespectral satellite images and RGB images obtained from a camera mounted on an unmanned aerial vehicle. Finally, the feasibility of merging information is demonstrated through an analysis of the correlation in the coverage measures obtained from satellite and low-altitude images. La deteccion, geolocalizacion y clasificacion de malezas en campos agricolas es un problema de interes asociado a la Agricultura de Precision (AP). El aporte principal de este trabajo es describir un flujo de trabajo (factible de automatizar) basado en herramientas de software libre e informacion abierta para: 1) medir la evolucion espaciotemporal de los parches de malezas a traves de imagenes satelitales y; 2) registrar las imagenes de alta resolucion (tomadas a baja altura) sobre la imagen satelital, para identificar las malezas que componen los parches detectados. Para fusionar la informacion satelital y de baja altura, se deben resolver los siguientes problemas: 1) corregir las distorsiones en las imagenes adquiridas; 2) desarrollar un modelo de formacion de imagenes que permita registrar la imagen a baja altura sobre la imagen satelital; 3) analizar los indices de verde para medir la cobertura de los parches, tanto en las imagenes satelitales multiespectrales, como en las imagenes RGB obtenidas desde una camara montada en un vehıculo aereo no tripulado. Finalmente, se muestra la factibilidad de realizar la fusion de informacion a partir de un analisis de la correlacion en las medidas de cobertura obtenidas de las imagenes satelitales y de las de baja altura. Sociedad Argentina de Informática e Investigación Operativa |
description |
The detection, geolocation, and classification of weeds in agricultural fields is a problem of interest associated with Precision Agriculture (PA). The main contribution of this work is to describe a workflow (feasible to automate) based on open-source software tools and open information to: 1) measure the spatiotemporal evolution of weed patches through satellite images, and 2) register high-resolution images (taken at low altitude) on top of the satellite image to identify the weeds that compose the detected patches. To merge the satellite and low-altitude information, the following problems must be solved: 1) correct distortions in the acquired images; 2) develop an image formation model that allows registering the low-altitude image on top of the satellite image, and 3) analyze green indices to measure patch coverage in both multiespectral satellite images and RGB images obtained from a camera mounted on an unmanned aerial vehicle. Finally, the feasibility of merging information is demonstrated through an analysis of the correlation in the coverage measures obtained from satellite and low-altitude images. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-06 |
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eng |
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eng |
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