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

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    Author
    Schnaufer, Christophe
    Pistorius, Julian L.
    LeBauer, David S.
    Affiliation
    Univ Arizona
    Issue Date
    2020-05-19
    
    Metadata
    Show full item record
    Publisher
    SPIE
    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
    Journal
    Proc. SPIE 11414, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V
    Rights
    © 2020 SPIE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    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.
    ISSN
    0277-786X
    DOI
    10.1117/12.2560008
    Version
    Final published version
    ae974a485f413a2113503eed53cd6c53
    10.1117/12.2560008
    Scopus Count
    Collections
    UA Faculty Publications

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