Advances in a Collaborative Robotic System for Field Phenotyping of
Reflectance and Canopy Temperature Depression from a UAV
Abstract
An autonomous mobile ground-control point (AMGCP) was redesigned and
refined for improved collaborative operation with an unmanned aerial
vehicle (UAV) to enable calibration of image mosaics from multispectral
(MS) and thermal cameras. The AMGCP has built-in reflectance panels and
electronically controlled thermal panels that provide high and low
reflectance and temperature references that can be used for calibration
of reflectance measurements in MS images and temperature measurements in
thermal-infrared images. The AMGCP also has an onboard temperature
sensor that enables image-based temperature measurements to be compared
to ambient temperature so that canopy temperature depression (CTD) can
be calculated. The collaborative robotic system consists of the AMGCP
and a UAV that have real-time kinematic (RTK) geographic positioning
system (GPS) receivers onboard so their precise position can be
determined in real time. The system also includes wireless communication
capability between the AMGCP and UAV so they can transmit their position
and other data to each other during a mission, in which the AMGCP
positions itself at multiple locations under the flight path of the UAV,
providing multiple instances of reflectance and temperature references
in image mosaics collected by the UAV. Testing has shown that
reflectance measurements can be calibrated to less than 1% reflectance
error, and canopy temperatures of crop plants can be calibrated to
within 1.0 C, enabling consistently accurate measurements to be made
efficiently and without human intervention in various fields and regions
and at different times and dates. This system is also suited to accurate
measurement of CTD to facilitate genetic selection relative to various
stresses and resilience characteristics like drought tolerance.