remote sensing Article Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery Ramin Heidarian Dehkordi 1,* , Moussa El Jarroudi 2 , Louis Kouadio 3 , Jeroen Meersmans 1 and Marco Beyer 4 1 TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium;
[email protected] 2 Department of Environmental Sciences and Management, University of Liège, 6700 Arlon, Belgium;
[email protected] 3 Centre for Applied Climate Sciences, University of Southern Queensland, West Street, Toowoomba, QLD 4350, Australia;
[email protected] 4 Luxembourg Institute of Science and Technology, 41 Rue du Brill, L-4422 Belvaux, Luxembourg;
[email protected] * Correspondence:
[email protected]; Tel.: +32-81-622-189 Received: 28 September 2020; Accepted: 7 November 2020; Published: 11 November 2020 Abstract: During the past decade, imagery data acquired from unmanned aerial vehicles (UAVs), thanks to their high spatial, spectral, and temporal resolutions, have attracted increasing attention for discriminating healthy from diseased plants and monitoring the progress of such plant diseases in fields. Despite the well-documented usage of UAV-based hyperspectral remote sensing for discriminating healthy and diseased plant areas, employing red-green-blue (RGB) imagery for a similar purpose has yet to be fully investigated. This study aims at evaluating UAV-based RGB imagery to discriminate healthy plants from those infected by stripe and wheat leaf rusts in winter wheat (Triticum aestivum L.), with a focus on implementing an expert system to assist growers in improved disease management. RGB images were acquired at four representative wheat-producing sites in the Grand Duchy of Luxembourg.