A team of scientists led byย Texas A&M AgriLifeย is taking a page from the medical imaging world and using MRI to examine crop roots in a quest to develop crops with stronger and deeper root systems.
The team fromย Texas A&M AgriLife Research,ย Harvard Medical School,ย ABQMRย Inc. andย Soil Health Instituteย developed a novel MRI-based root phenotyping system to nondestructively acquire high-resolution images of plant roots growing in soil and established theย Texas A&M Roots Labย to further develop this technology as a new tool for assessing crop root traits.
The โField-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Functionโ is a part of the Rhizosphere Observations Optimizing Terrestrial Sequestration, ROOTS, program funded throughย U.S. Department of Energyโsย Advanced Research Projects Agency-Energyย program.
Nithya Rajan, Ph.D., AgriLife Research crop physiologist/agroecologist in theย College of Agriculture and Life Sciencesย Department of Soil and Crop Sciences, Bryan-College Station, is leading this multidisciplinary project team.
โWe are applying this technology to see if we can sense roots growing in agricultural soils and characterize them,โ she said. โTo date, imaging roots in soil has been challenging because the soil is complex, with solids, moisture and roots. We just want to image the roots.โ
We need to develop crop root systems that store more carbon in soil. In addition, deeper root systems can take up more water from soil profiles, increasing crop drought resilience.
John Mullet, Ph.D., biochemist and Perry L. Adkisson Chair in Agricultural Biology in theย Department of Biochemistry and Biophysics
From concept to applications, in sorghum and beyond
The project was initially funded for three years with a $4.6 million grant. The second phase of funding was approved this year at $4.4 million.
โIn the first phase, we developed the proof of concept and initial prototypes, and in the second phase we developed a low-field MRI rhizotron for high throughput imaging and applications in a wide variety of crops in addition to energy sorghum,โ Rajan said.
Also on the team with AgriLife Research areย Bill Rooney, Ph.D., sorghum breeder and Borlaug-Monsanto Chair for Plant Breeding and International Crop Improvement in the Department of Soil and Crop Sciences, andย John Mullet, Ph.D., biochemist and Perry L. Adkisson Chair in Agricultural Biology in theย Department of Biochemistry and Biophysics.
Rooney and Mullet are using the MRI system to advance bioenergy sorghum genetics. Brock Weers, Ph.D., and Will Wheeler, Ph.D., are support scientists working with the AgriLife Research team.
โWe need to develop crop root systems that store more carbon in soil,โ Mullet said. โIn addition, deeper root systems can take up more water from soil profiles, increasing crop drought resilience.โ
From a crop improvement perspective, Rooney added, this technology is essential to effectively screen crop germplasm for specific genotypes with enhanced root systems.
Getting to the root of the matter, without disturbing the soil
Using MRI allows researchers to gather root images without damaging plants, unlike traditional methods such as trenching, soil coring and root excavation, Rajan said.
The AgriLife Research team is working with ABQMR Inc., a group of MRI scientists in Albuquerque, New Mexico, who are involved in designing and building the system.
โWith low magnetic fields, MRI can be used to image roots in natural soils,โ said Hilary Fabich, Ph.D., president of ABQMR. โThe low magnetic fields also mean there is less of a safety risk working with the sensor in an agricultural setting.โ
Using โmachine learningโ to see through the noise
Matt Rosen, Ph.D., is the co-principal investigator of the project. He is director of the Low-field MRI and Hyperpolarized Media Laboratory and co-director of theย Center for Machine Learning at the Martinos Center for Biomedical Imagingย at Harvard. Rosen and his team bring their experience with both low-field MRI physics and state-of-the-art artificial intelligence techniques to the project.
The Rosen labย pioneered the use of deep learning for processing MRI data. Neha Koonjoo, Ph.D., a postdoctoral fellow in the Rosen lab, has been leveraging the AUTOMAP โ Automated TransfOrm by Manifold Approximation โ deep learning-based image reconstruction approach to reduce the influence of environmental noise in root MRI images. Her approach was described in a recentย research article. ย
Bragi Sveinsson, Ph.D., a postdoctoral fellow working with Rosen, developed the first prototype of a software named โMIDGARDโ โ MRI 3D seGmentation and Analysis for Root Description โ for extracting quantitative root trait information from MRI images of roots.
The team plans to release MIDGARD as an open-source software after further testing.
โUsing MIDGARD, we can extract quantitative root trait information, and this data will be used for selection of ideal plant characteristics,โ Rosen said. โIn the future, MIDGARD will also have the ability to three-dimensionally image soil water content, a key property that drives root growth and exploration.โ
Technology to market
Technology-to-market activities of this project are led byย Cristine Morgan, Ph.D., chief scientific officer of Soil Health Institute, Research Triangle Park, North Carolina, and principal investigator of the first phase of the project when she was at Texas A&M. To foster collaborations with industry partners, the Soil Health Institute established the company Intact Data Services.
โI am excited to translate this technology for phenotyping at scale, as well as the ability to use MRI to 3D-image soil water intact,โ Morgan said.
