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

2019; Multidisciplinary Digital Publishing Institute; Volume: 3; Issue: 2 Linguagem: Inglês

10.3390/drones3020033

2504-446X

Thomaz Willian de Figueiredo Xavier, Roberto Nunes Vianconi Souto, Thiago Statella, Rafael Galbieri, Emerson Soares dos Santos, George Sanches Suli, Peter Zeilhofer,

Forest Insect Ecology and Management

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.