Combined UAV‐ and tractor‐based stripe rust monitoring in winter wheat under field conditions. (16th December 2021)

• Record Type: Journal Article
• Authors: Dammer, Karl‐Heinz; Garz, Andreas; Hobart, Marius; Schirrmann, Michael
• Is Part Of: Agronomy Journal. Volume 114:Number 1(2022)
• Page Start: 651
• Page End: 661
• Publication Date: 2021-12-16
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.1002/agj2.20916
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract: Unmanned aerial vehicles (UAVs) have the potential to monitor the health status of several crop fields of a farmer in full coverage. For a complete and fast monitoring, however, high flight altitudes are usually needed, especially if large areas should be observed in short time intervals. In this case, the resolution on the ground becomes insufficient to detect specific symptoms of crop diseases because a ground resolution in the submillimeter scale is needed. This study pursued the idea of performing remote UAV imaging to detect discoloration in combination with near‐surface tractor imaging to detect the uredospore layers as characteristic signs of stripe (yellow) rust (caused by Puccinia striiformis Westend. f. sp. tritici ) in winter wheat ( Triticum aestivum L.). To simulate healthy and diseased field parts, the 3‐yr experimental design included controlled infected plots and those sprayed with fungicides as healthy controls. Imaging, disease severity, and crop development rating were performed along a time series. Significant differences between infected and control plots occurred in the UAV imagery using the normalized green–red difference index from a median (upper three leaves) infested leaf area of 3% and for the tractor images using the maximally stable extreme regions detector from 3 and 5%, respectively. In the future, it is conceivable that farmers will combine UAV (aerial monitoring of crop damage of complete fields) and tractor (ground monitoring to determine the cause) imaging for automatic scanning of the health status. Core Ideas: Unmanned aerial vehicle (UAV) images showed discoloration in the infected plots. Normalized green–red difference index (NGRDI) from UAV images was lower in the infected plots. Tractor images showed the uredospore layers of stripe rust on the leaves. Number of maximally stable extremal regions (MSERs) from tractor images were higher in the infected plots. Large‐scale UAV field monitoring should be combined with tractor ground verification.