Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic objec...

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Autores principales:	José Manuel Peña, Jorge Torres-Sánchez, Ana Isabel de Castro, Maggi Kelly, Francisca López-Granados
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/217c9e496b374125a91b3ce9b3696b19
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id	oai:doaj.org-article:217c9e496b374125a91b3ce9b3696b19
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spelling	oai:doaj.org-article:217c9e496b374125a91b3ce9b3696b192021-11-18T08:51:23ZWeed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.1932-620310.1371/journal.pone.0077151https://doaj.org/article/217c9e496b374125a91b3ce9b3696b192013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24146963/?tool=EBIhttps://doaj.org/toc/1932-6203The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.José Manuel PeñaJorge Torres-SánchezAna Isabel de CastroMaggi KellyFrancisca López-GranadosPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 10, p e77151 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q José Manuel Peña Jorge Torres-Sánchez Ana Isabel de Castro Maggi Kelly Francisca López-Granados Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.
description	The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.
format	article
author	José Manuel Peña Jorge Torres-Sánchez Ana Isabel de Castro Maggi Kelly Francisca López-Granados
author_facet	José Manuel Peña Jorge Torres-Sánchez Ana Isabel de Castro Maggi Kelly Francisca López-Granados
author_sort	José Manuel Peña
title	Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.
title_short	Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.
title_full	Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.
title_fullStr	Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.
title_full_unstemmed	Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.
title_sort	weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (uav) images.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/217c9e496b374125a91b3ce9b3696b19
work_keys_str_mv	AT josemanuelpena weedmappinginearlyseasonmaizefieldsusingobjectbasedanalysisofunmannedaerialvehicleuavimages AT jorgetorressanchez weedmappinginearlyseasonmaizefieldsusingobjectbasedanalysisofunmannedaerialvehicleuavimages AT anaisabeldecastro weedmappinginearlyseasonmaizefieldsusingobjectbasedanalysisofunmannedaerialvehicleuavimages AT maggikelly weedmappinginearlyseasonmaizefieldsusingobjectbasedanalysisofunmannedaerialvehicleuavimages AT franciscalopezgranados weedmappinginearlyseasonmaizefieldsusingobjectbasedanalysisofunmannedaerialvehicleuavimages
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Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images.

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