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.
Highlights
Remote sensing has been proven to be a key technology in monitoring cotton (Gossypium hirsutum L.)crop yields [1,2], nutrient status [3,4], water stress [5,6], and diseases [7,8,9,10,11]
We evaluate how different flight heights affect the separability measures between infection classes to guide potential users in balancing between adequate spatial resolution and terrain coverage during unmanned airborne vehicle (UAV) operation
The modest fa increased in the visual bands and the slight reduction in the NIR were caused by higher sensor exposure times at higher flight heights, which reduced the DCnor estimates and compensated for increasing path radiance
Summary
Remote sensing has been proven to be a key technology in monitoring cotton (Gossypium hirsutum L.)crop yields [1,2], nutrient status [3,4], water stress [5,6], and diseases [7,8,9,10,11]. Remote sensing has been proven to be a key technology in monitoring cotton (Gossypium hirsutum L.). The focus has been mainly on the application of field spectroscopy at the canopy scale [8,12,13,14,15,16] and manned airborne systems [2,17] to support precision farming approaches. Specific cotton-related orbital remote sensing studies are rare [18,19,20]. Most satellite-based approaches, including the identification of cotton crops, are broader land-use and land-cover studies or biomass estimates without the characterization of the physical, biological, or chemical conditions of the crop [21,22]. Previous studies on remote pest detection mainly focused on cotton rot [17,23] or the identification of crop diseases on the leaf scale [24].
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