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

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.

Governance and stewardship for research data and information sharing: Issues and prospective solutions in the transdisciplinary plant phenotyping and imaging research center network

“…In addition, the use of standard classifications and vocabularies plays an important role in the ability of people and machines to easily find, access and integrate data from different applications and systems (Germeier & Unger, 2019). Other examples of standards that are used by the plant phenotyping community include: Climate Foreacs—a metadata conventions and NetCDF‐CF file format; Open Geospatial Consortium, which provides different standards such as GeoTIFF image formats and geojson file formats; Breeder's API—a specification for breeding databases interoperability that provides all information required by MIAPPE; and AgMIP Crop Experiment formats that provides agronomic, experimental and environmental metadata (Schnaufer et al, 2020).…”

What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?

“…The computing pipeline has been adapted and extended for continuing use with the Field Scanner with the new name "PhytoOracle" and is available at https: //github.com/LyonsLab/PhytoOracle. Related work generalizing the pipeline for other phenomics applications has been released under the name "AgPipeline" https://github.com/agpipeline with applications to aerial imaging described by Schnaufer et al [21]. All of these software are made available with permissive open source licenses on GitHub to enable access and com-munity development.…”

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

“…Researchers have developed several solutions addressing these challenges. A few examples of semiautomated and automated analysis of UAS data for small‐plot research include ImageBreed (Morales et al., 2020), pheno‐image analysis (Selvaraj et al., 2020), FIELDImageR (Matias et al., 2020), drone processing pipeline (Schnaufer et al., 2020), R/UAS tools (Anderson & Murray, 2020), and Bison‐Fly (Matias et al., 2022). These overall processes are further discussed and compared in their respective papers and will not be described here.…”