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Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management

The perennial and stoloniferous weed, Cynodon dactylon (L.) Pers. (bermudagrass), is a serious problem in vineyards. The spectral similarity between bermudagrass and grapevines makes discrimination of the two species, based solely on spectral information from multi-band imaging sensor, unfeasible. H...

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Autores principales: Jiménez-Brenes, Francisco Manuel, López-Granados, Francisca, Torres-Sánchez, Jorge, Peña, José Manuel, Ramírez, Pilar, Castillejo-González, Isabel Luisa, de Castro, Ana Isabel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6559662/
https://www.ncbi.nlm.nih.gov/pubmed/31185068
http://dx.doi.org/10.1371/journal.pone.0218132
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author Jiménez-Brenes, Francisco Manuel
López-Granados, Francisca
Torres-Sánchez, Jorge
Peña, José Manuel
Ramírez, Pilar
Castillejo-González, Isabel Luisa
de Castro, Ana Isabel
author_facet Jiménez-Brenes, Francisco Manuel
López-Granados, Francisca
Torres-Sánchez, Jorge
Peña, José Manuel
Ramírez, Pilar
Castillejo-González, Isabel Luisa
de Castro, Ana Isabel
author_sort Jiménez-Brenes, Francisco Manuel
collection PubMed
description The perennial and stoloniferous weed, Cynodon dactylon (L.) Pers. (bermudagrass), is a serious problem in vineyards. The spectral similarity between bermudagrass and grapevines makes discrimination of the two species, based solely on spectral information from multi-band imaging sensor, unfeasible. However, that challenge can be overcome by use of object-based image analysis (OBIA) and ultra-high spatial resolution Unmanned Aerial Vehicle (UAV) images. This research aimed to automatically, accurately, and rapidly map bermudagrass and design maps for its management. Aerial images of two vineyards were captured using two multispectral cameras (RGB and RGNIR) attached to a UAV. First, spectral analysis was performed to select the optimum vegetation index (VI) for bermudagrass discrimination from bare soil. Then, the VI-based OBIA algorithm developed for each camera automatically mapped the grapevines, bermudagrass, and bare soil (accuracies greater than 97.7%). Finally, site-specific management maps were generated. Combining UAV imagery and a robust OBIA algorithm allowed the automatic mapping of bermudagrass. Analysis of the classified area made it possible to quantify grapevine growth and revealed expansion of bermudagrass infested areas. The generated bermudagrass maps could help farmers improve weed control through a well-programmed strategy. Therefore, the developed OBIA algorithm offers valuable geo-spatial information for designing site-specific bermudagrass management strategies leading farmers to potentially reduce herbicide use as well as optimize fuel, field operating time, and costs.
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spelling pubmed-65596622019-06-17 Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management Jiménez-Brenes, Francisco Manuel López-Granados, Francisca Torres-Sánchez, Jorge Peña, José Manuel Ramírez, Pilar Castillejo-González, Isabel Luisa de Castro, Ana Isabel PLoS One Research Article The perennial and stoloniferous weed, Cynodon dactylon (L.) Pers. (bermudagrass), is a serious problem in vineyards. The spectral similarity between bermudagrass and grapevines makes discrimination of the two species, based solely on spectral information from multi-band imaging sensor, unfeasible. However, that challenge can be overcome by use of object-based image analysis (OBIA) and ultra-high spatial resolution Unmanned Aerial Vehicle (UAV) images. This research aimed to automatically, accurately, and rapidly map bermudagrass and design maps for its management. Aerial images of two vineyards were captured using two multispectral cameras (RGB and RGNIR) attached to a UAV. First, spectral analysis was performed to select the optimum vegetation index (VI) for bermudagrass discrimination from bare soil. Then, the VI-based OBIA algorithm developed for each camera automatically mapped the grapevines, bermudagrass, and bare soil (accuracies greater than 97.7%). Finally, site-specific management maps were generated. Combining UAV imagery and a robust OBIA algorithm allowed the automatic mapping of bermudagrass. Analysis of the classified area made it possible to quantify grapevine growth and revealed expansion of bermudagrass infested areas. The generated bermudagrass maps could help farmers improve weed control through a well-programmed strategy. Therefore, the developed OBIA algorithm offers valuable geo-spatial information for designing site-specific bermudagrass management strategies leading farmers to potentially reduce herbicide use as well as optimize fuel, field operating time, and costs. Public Library of Science 2019-06-11 /pmc/articles/PMC6559662/ /pubmed/31185068 http://dx.doi.org/10.1371/journal.pone.0218132 Text en © 2019 Jiménez-Brenes et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jiménez-Brenes, Francisco Manuel
López-Granados, Francisca
Torres-Sánchez, Jorge
Peña, José Manuel
Ramírez, Pilar
Castillejo-González, Isabel Luisa
de Castro, Ana Isabel
Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management
title Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management
title_full Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management
title_fullStr Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management
title_full_unstemmed Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management
title_short Automatic UAV-based detection of Cynodon dactylon for site-specific vineyard management
title_sort automatic uav-based detection of cynodon dactylon for site-specific vineyard management
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6559662/
https://www.ncbi.nlm.nih.gov/pubmed/31185068
http://dx.doi.org/10.1371/journal.pone.0218132
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