Remotely sensed vegetation data provide an effective means of measuring the spatial variability of nitrogen and therefore of managing applications by taking intrafield variations into account. Satellites, drones and sensors mounted on agricultural machinery are all technologies that can be used for this purpose. Although a drone (or unmanned aerial vehicle [UAV]) can produce very high-resolution images, the comparative advantages of this type of imagery have not been demonstrated. The goal of this study was to assess the potential benefits associated with the high spatial resolution (5 cm per pixel) of drone-acquired images in comparison to a proximal sensor used for nitrogen status determination in corn. A series of images were acquired over two commercial fields in June 2015. The corn phenological stages at the time of data acquisition ranged from V4 to V6. Images were acquired from a UAV (eBee fixed-wing drone) and from GreenSeeker onboard sensors. The UAV was operated with a modified commercial camera: the Canon S110 NIR (550 nm, 625 nm and 850 nm). Field measurement campaigns were carried out and coordinated with image acquisition in order to obtain quantitative measurements of the biophysical parameters governing vegetation conditions (biomass and leaf area index [LAI]). To assess the potential benefits of image segmentation, a comparative analysis of normalized difference vegetation index (NDVI) maps produced from the GreenSeeker and UAV data was carried out. NDVI maps generated from UAV imagery contained higher spatial detail than those produced by GreenSeeker, but both technologies had good relationships with biomass and LAI. The GreenSeeker R2 relationships with biophysical parameters outperformed those of the UAV.
