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This is a Preprint and has not been peer reviewed.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.

Downloads

Download Preprint

Supplementary Files
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 emphasizes open software and data that can be shared among and used in whole or part by the research community. We are building the DPP as a distributed, scalable, and flexible pipeline for converting drone imagery into orthomosaics, point clouds, and plot level statistics. Our intent is not to replace, but to integrate components from the emerging ecosystem of utilities with a focus on end-to-end automation and scalability.

The initial focus of DPP is the measurements of experimental plots in field research.
In the future we expect that standardization will enable new scientific discovery by facilitating collaboration and sharing of software and data. Our vision is to create a processing pipeline that is open, flexible, extensible, portable, and automated. With modern tools, deploying a pipeline on a laptop or HPC should only take a single command. Running a pipeline and publishing data should require only input data and a defined workflow.

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 02:23

Older Versions
License

CC BY Attribution 4.0 International

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.