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Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, th...

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Detalles Bibliográficos
Autores principales: Lelong, Camille C. D., Burger, Philippe, Jubelin, Guillaume, Roux, Bruno, Labbé, Sylvain, Baret, Frédéric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675559/
https://www.ncbi.nlm.nih.gov/pubmed/27879893
http://dx.doi.org/10.3390/s8053557
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author Lelong, Camille C. D.
Burger, Philippe
Jubelin, Guillaume
Roux, Bruno
Labbé, Sylvain
Baret, Frédéric
author_facet Lelong, Camille C. D.
Burger, Philippe
Jubelin, Guillaume
Roux, Bruno
Labbé, Sylvain
Baret, Frédéric
author_sort Lelong, Camille C. D.
collection PubMed
description This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.
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spelling pubmed-36755592013-06-19 Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots Lelong, Camille C. D. Burger, Philippe Jubelin, Guillaume Roux, Bruno Labbé, Sylvain Baret, Frédéric Sensors (Basel) Article This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships. Molecular Diversity Preservation International (MDPI) 2008-05-26 /pmc/articles/PMC3675559/ /pubmed/27879893 http://dx.doi.org/10.3390/s8053557 Text en © 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Lelong, Camille C. D.
Burger, Philippe
Jubelin, Guillaume
Roux, Bruno
Labbé, Sylvain
Baret, Frédéric
Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
title Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
title_full Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
title_fullStr Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
title_full_unstemmed Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
title_short Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
title_sort assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675559/
https://www.ncbi.nlm.nih.gov/pubmed/27879893
http://dx.doi.org/10.3390/s8053557
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