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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Publications at this Location » Publication #371973
Research Project: Molecular Genetic and Proximal Sensing Analyses of Abiotic Stress Response and Oil Production Pathways in Cotton, Oilseeds, and Other Industrial and Biofuel Crops

Location: Plant Physiology and Genetics Research

Title: A data workflow to support plant breeding decisions from a terrestrial field-based high-throughput plant phenotyping system

Author
item Thompson, Alison
item Thorp, Kelly
item Conley, Matthew
item Roybal, Michael
item Moller Jr, David
item Long, Jacob

Submitted to: Plant Methods
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/9/2020
Publication Date: 7/16/2020
Citation: Thompson, A.L., Thorp, K.R., Conley, M.M., Roybal, M.D., Moller Jr, D.C., Long, J.C. 2020. A data workflow to support plant breeding decisions from a terrestrial field-based high-throughput plant phenotyping system. Plant Methods. 16. Article 97. https://doi.org/10.1186/s13007-020-00639-9.
DOI: https://doi.org/10.1186/s13007-020-00639-9

Interpretive Summary: The main objective of this research was to develop a workflow for management of data from a sensing system on a high-clearance tractor, used primarily to support cotton breeding objectives at the USDA-ARS research station in Maricopa, Arizona. Specific objectives were to 1) describe each aspect of the workflow, including data collection, database design, geospatial processing, quality control, visualization, and outlier removal and 2) demonstrate the value of the methodology as applied to the cotton breeding program at Maricopa, Arizona.

Technical Abstract: Field-based high-throughput plant phenotyping (FB-HTPP) has been a primary focus for crop improvement to meet the demands of a growing population in a changing environment. Over the years, breeders, geneticists, physiologists, and agronomists have been able to improve the understanding between complex dynamic traits and plant response to changing environmental conditions using FB-HTPP. However, the volume, velocity, and variety of data captured by FB-HTPP can be problematic, requiring large data stores, databases, and computationally intensive data processing pipelines. To be fully effective, FB-HTTP data workflows including applications for database implementation, data processing, and data interpretation must be developed and optimized. At the US Arid Land Agricultural Center in Maricopa Arizona, USA a data workflow was developed for a terrestrial FB-HTPP platform that utilized a custom Python application and a PostgreSQL database. The workflow developed for the HTPP platform enables users to capture and organize data and verify data quality before statistical analysis. The data from this platform and workflow were used to identify plant lodging and heat tolerance, enhancing genetic gain by improving selection accuracy in an upland cotton breeding program. An advantage of this platform and workflow was the increased amount of data collected throughout the season, while a main limitation was the start-up cost.