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ARS Home » Pacific West Area » Wapato, Washington » Temperate Tree Fruit and Vegetable Research » Research » Publications at this Location » Publication #392791

Research Project: Developing New Potatoes with Improved Quality, Disease Resistance, and Nutritional Content

Location: Temperate Tree Fruit and Vegetable Research

Title: Bison-Fly: An open-source UAV pipeline for plant breeding data collection

item MATIAS, FILIPE - North Dakota State University
item GREEN, ANDREW - North Dakota State University
item LACHOWIEC, JENNIFER - Montana State University
item LEBAUER, DAVID - University Of Arizona
item Feldman, Max

Submitted to: The Plant Phenome Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/25/2022
Publication Date: 7/31/2022
Citation: Matias, F.I., Green, A., Lachowiec, J., LeBauer, D.S., Feldman, M.J. 2022. Bison-Fly: An open-source UAV pipeline for plant breeding data collection. The Plant Phenome Journal. 5(1). Article e20048.

Interpretive Summary: Data collection using unoccupied aerial vehicles (UAV) is becoming increasingly common place within plant breeding programs. Scientists at the USDA-ARS laboratory in Prosser, WA in collaboration with researchers at North Dakota State University, Montana State University, and the University of Arizona have developed a step-by-step protocol to collect and analyze data captured using UAVs. This open-source analysis pipeline will facilitate the collection and processing of UAV derived data and enable researchers to apply this technology to quantitatively evaluate hundreds of field grown breeding lines at multiple points throughout the field season. The computer codes associated with this manuscript can be easily adapted by students, researchers, and plant breeders to collect measurements of diverse field crops and other agricultural commodities.

Technical Abstract: Bison-Fly is the pipeline of unoccupied aerial vehicle (UAV) applications for plant breeding developed by the Spring Wheat Breeding Program at North Dakota State University (NDSU) in partnership with the Drone2Phenome community (D2P). This pipeline presents a step-by-step process to collect, process, and apply UAV data for indirect selection to facilitate data processing, reduce errors, and create new traits. This open-source R code can be easily adapted for different crops and improved according to user requirements. We hope this pipeline helps guide students, researchers, and breeders on day-to-day activities.