Rays for Roots Integrating Backscatter X-Ray Phenotyping, Modeling, and Genetics to Increase Carbon Sequestration and Switchgrass Resource Use Efficiency

Principal Investigator: Alina Zare

Sponsor: ARPA-E

Start Date: August 25, 2017

End Date: August 24, 2020

Amount: $5,965,000


The University of Florida will develop a backscatter X-ray platform to non-destructively image roots in field conditions. The team will focus their efforts on switchgrass, a promising biofuel feedstock with deep and extensive root systems. Switchgrass is also a good candidate to study because it is a perennial grass with great genetic diversity that is broadly adapted to the full range of environments found in the U.S. The project will leverage a DOE-funded switchgrass common garden with ten identical plantings that span growth zones from Texas to South Dakota. X-ray backscatter systems use a targeted beam to illuminate the part of the plant under observation, and sensors detect the x-rays reflected back to construct an image. The system will not require trenches or other modifications to the field, and will be able to provide three-dimensional root and soil imaging. Software developed by the team will help refine the raw data collected. Image processing and machine learning algorithms will improve image formation and autonomously analyze and extract key root and soil characteristics. In particular, root-vs-soil segmentation algorithms will be developed to identify roots in the imagery and extract geometric-based features such as root length and root diameter. Statistical machine learning algorithms will also be developed and trained to extract information from the imagery beyond the geometric-based features traditionally identified. The project aims to identify the genetic and environmental factors associated with desirable root characteristic that can lead to increased carbon flow and deposition into the soil. If the team is successful, these tools will be broadly applicable to other crops and application areas beyond switchgrass.

More Information: https://arpa-e.energy.gov/?q=slick-sheet-project/backscatter-x-ray-phenotyping