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Open AccessFeature PaperCommunication

UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras

1
Department of Agricultural and Forestry Engineering, ETSIIAA, Universidad de Valladolid, 34004 Palencia, Spain
2
Unidad de Suelos y Riegos (asociada a EEAD-CISC), Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), 50059 Zaragoza, Spain
3
Department of Technical Education. European University Miguel de Cervantes, 47012 Valladolid, Spain
4
Departamento de Ciências da Terra, Universidade de Coimbra, Rua Sílvio Lima, 3030-790 Coimbra, Portugal
5
CITEUC—Centro de Investigação da Terra e do Espaço, Universidade de Coimbra, Rua do Observatório, 3040-004 Coimbra, Portugal
6
Instituto Universitario de Investigación en Ciencias Ambientales de Aragón (IUCA), EPS, Universidad de Zaragoza, 22071 Huesca, Spain
*
Author to whom correspondence should be addressed.
AgriEngineering 2020, 2(2), 206-212; https://doi.org/10.3390/agriengineering2020012
Received: 13 February 2020 / Revised: 27 March 2020 / Accepted: 30 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue Selected Papers from 10th Iberian Agroengineering Congress)
Unmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps. View Full-Text
Keywords: precision agriculture; remote sensing; RGB sensor; unmanned aerial vehicle; weed precision agriculture; remote sensing; RGB sensor; unmanned aerial vehicle; weed
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Sánchez-Sastre, L.F.; Casterad, M.A.; Guillén, M.; Ruiz-Potosme, N.M.; Veiga, N.M.S.A.; Navas-Gracia, L.M.; Martín-Ramos, P. UAV Detection of Sinapis arvensis Infestation in Alfalfa Plots Using Simple Vegetation Indices from Conventional Digital Cameras. AgriEngineering 2020, 2, 206-212.

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