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An open, scalable, and flexible framework for automated aerial measurement of field experiments

An open, scalable, and flexible framework for automated aerial measurement of field experiments

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1117/12.2560008. This is version 2 of this Preprint.

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Authors

Christophe Schnaufer, Julian Pistorius, David Shaner LeBauer

Abstract

Unoccupied areal vehicles (UAVs or drones) are increasingly used in field research. Drones capable of routinely and consistently capturing high quality imagery of experimental fields have become relatively inexpensive. However, converting these images into scientifically useable data has become a bottleneck. A number of tools exist to support this workflow, but there is no framework for making these tools interopreable, sharable, and scalable.

Here we present an initial draft of the Drone Processing Pipeline (DPP), a framework for processing agricultural research imagery that supports best practices and interoperability. DPP emphasize...  more

DOI

https://doi.org/10.31223/osf.io/vezn5

Subjects

Agricultural Science, Agriculture, Bioresource and Agricultural Engineering, Ecology and Evolutionary Biology, Engineering, Life Sciences, Plant Breeding and Genetics Life Sciences, Plant Sciences, Research Methods in Life Sciences, Terrestrial and Aquatic Ecology

Keywords

agriculture, automation, data standards, ecosystem ecology, high performance computing, high throughput phenotyping, open source software, unoccupied aerial vehicles, workflows

Dates

Published: 2020-05-25 10:23

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License

CC BY Attribution 4.0 International