The Assessment of Accuracy and Stability for a UAS Sensor Platform as a Precision Agriculture Management Tool in Detecting and Mapping Geospatial Field Variability

摘要

The unmanned aerial system (UAS), that is used as a precision agricultural management tool for detecting and mapping geospatial variability in the crop health of the agricultural row field, is dependent on global positioning system (GPS) receivers andremote sensor (RS) systems. Unlike a ground platform, the UAS is subject to additional platform factors such as autonomous flight system, inertial navigational system (INS), aerodynamics, and aerial climate conditions which can affect flight path accuracy and stability. Such factors can result in diminished quality of trajectory accuracy or aerial Nadir stability that is needed for the precision mapping of geospatial variability zones that are used in the management decisions for geospatial input of treatments. The diminished quality of the UAS flight path can in turn cause RS image-stitching distortions that will render a precision treatment zone map geospatially insufficientUsing the scientific method, this project proposes bringing geospatial assessment tools to the agricultural field that serve to independently verify the flight path accuracy and stability aspects of any UAS sensor platform apart from sole reliance on ortho-stitching processing or sole reliance on on-board platform systems. In a field, a set pattern of elevation posts with global navigational satellite system (GNSS) obtained coordinates will be erected to be viewed from the Nadir orientation of any UAS.With any UAS autonomous platform equipped for optically tracking geospatial flight path metrics, the UAS flight path will follow the posted field pattern at various altitudes. The expected result will be a repeatable testing regimen that will be suitablein standardizing the assessment of any UAS sensor platform for flight path stability and accuracy where precision mapping and true global accuracy are needed such as in the management decisions for the geospatial input of treatments.