In apple cultivation, spatial information about phenotypic characteristics of tree walls would be beneficial for precise orchard management. Unmanned aerial vehicles (UAVs) can collect 3D structural information of ground surface objects at high resolution in a cost-effective and versatile way by using photogrammetry. The aim of this study is to delineate tree wall height information in an apple orchard applying a low-altitude flight pattern specifically designed for UAVs. This flight pattern implies small distances between the camera sensor and the tree walls when the camera is positioned in an oblique view toward the trees. In this way, it is assured that the depicted tree crown wall area will be largely covered with a larger ground sampling distance than that recorded from a nadir perspective, especially regarding the lower crown sections. Overlapping oblique view images were used to estimate 3D point cloud models by applying structure-from-motion (SfM) methods to calculate tree wall heights from them. The resulting height models were compared with ground-based light detection and ranging (LiDAR) data as reference. It was shown that the tree wall profiles from the UAV point clouds were strongly correlated with the LiDAR point clouds of two years (2018: R2 = 0.83; 2019: R2 = 0.88). However, underestimation of tree wall heights was detected with mean deviations of −0.11 m and −0.18 m for 2018 and 2019, respectively. This is attributed to the weaknesses of the UAV point clouds in resolving the very fine shoots of apple trees. Therefore, the shown approach is suitable for precise orchard management, but it underestimated vertical tree wall expanses, and widened tree gaps need to be accounted for.
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.
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