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

Christophe Schnaufer; Julian L. Pistorius; David S. LeBauer

doi:10.1117/12.2560008

19 May 2020 An open, scalable, and flexible framework for automated aerial measurement of field experiments

Christophe Schnaufer, Julian L. Pistorius, David S. LeBauer

Proceedings Volume 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V; 114140A (2020) https://doi.org/10.1117/12.2560008
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

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 work ow, 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 work flow.

Conference Presentation

Citation Download Citation

Christophe Schnaufer, Julian L. Pistorius, and David S. LeBauer "An open, scalable, and flexible framework for automated aerial measurement of field experiments", Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V, 114140A (19 May 2020); https://doi.org/10.1117/12.2560008

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