Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

Authors

Brian Krienke, University of Nebraska-LincolnFollow
Richard B. Ferguson, University of Nebraska-LincolnFollow
Michael Schlemmer, Bayer Crop ScienceFollow
Kyle Holland, Holland Scientific Inc.
David B. Marx, University of Nebraska-LincolnFollow
Kent M. Eskridge, University of Nebraska-LincolnFollow

ORCID IDs

Kent M. Eskridge

Document Type

Article

Date of this Version

2017

Citation

Published in Precision Agriculture 18 (2017), pp. 900–915. doi 10.1007/s11119-017-9534-5

Comments

Copyright © 2017 Springer Science+Business Media, LLC. Used by permission.

Abstract

Ground-based active sensors have been used in the past with success in detecting nitrogen (N) variability within maize production systems. The use of unmanned aerial vehicles (UAVs) presents an opportunity to evaluate N variability with unique advantages compared to ground-based systems. The objectives of this study were to: determine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize, if UAV’s were a suitable platform, is the UAV and active sensor platform a suitable substitute for current handheld methods, and is there a height effect that may be confounding measurements of N status over crop canopies? In a 2013 study comparing aerial and ground-based sensor platforms, there was no difference in the ability of aerial and ground-based active sensors to detect N rate effects on a maize crop canopy. In a 2014 study, an active sensor mounted on a UAV was able to detect differences in crop canopy N status similarly to a handheld active sensor. The UAV/active sensor system (AerialActive) platform used in this study detected N rate differences in crop canopy N status within a range of 0.5–1.5 m above a relatively uniform turfgrass canopy. The height effect for an active sensor above a crop canopy is sensor- and crop-specific, which needs to be taken into account when implementing such a system. Unmanned aerial vehicles equipped with active crop canopy sensors provide potential for automated data collection to quantify crop stress in addition to passive sensors currently in use.