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Drones 2019, 3(2), 33; https://doi.org/10.3390/drones3020033

Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery

1
Department of Geography, Federal University of Ceará, Fortaleza CE 60440-554, Brazil
2
Federal Institute of Mato Grosso, Cuiabá, MT 78043-400, Brazil
3
Mato Grosso Cotton Institute, Primavera do Leste, MT 78850-000, Brazil
4
Department of Geography, Federal University of Mato Grosso, Cuiabá, MT 78068-600, Brazil
5
Department of Physics, Federal University of Mato Grosso, Cuiabá, MT 78060-900, Brazil
*
Authors to whom correspondence should be addressed.
Received: 19 February 2019 / Revised: 27 March 2019 / Accepted: 1 April 2019 / Published: 2 April 2019
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
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Abstract

The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton.
Keywords: disease severity assessment; ground cover; UAV; ramularia areola; remote sensing disease severity assessment; ground cover; UAV; ramularia areola; remote sensing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xavier, T.W.F.; Souto, R.N.V.; Statella, T.; Galbieri, R.; Santos, E.S.; Suli, G.S.; Zeilhofer, P. Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery. Drones 2019, 3, 33.

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