Slick Sheet: Project
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.

Slick Sheet: Project
Pennsylvania State University (Penn State) will develop DEEPER, a platform for identifying the traits of deeper-rooted crops that integrates breakthroughs in nondestructive field phenotyping of rooting depth, root modeling, high-throughput 3D imaging of root architecture and anatomy, gene discovery, and genomic selection modeling. The platform will be deployed to observe maize (corn) in the field under drought, nitrogen stress, and non-stressed conditions. Their key sensor innovation is to measure leaf elemental composition with x-ray fluorescence, and use it as a proxy for rooting depth.

Slick Sheet: Project
Stanford University will develop a non-contact root imaging system that uses a hybrid of microwave excitation and ultrasound detection. Microwave excitation from the surface can penetrate the soil to the roots, and results in minor heating of the roots and soil at varying levels depending on their physical properties. This heating creates a thermoacoustic signal in the ultrasound domain that travels back out of the soil. The team’s advanced ultrasound detector has the ability to detect these signals and maintain sufficient signal-to-noise ratio for imaging and root biomass analysis.

Slick Sheet: Project
UHV Technologies will develop and demonstrate a low cost, field deployable 3D x-ray computed tomography system that will image total root systems in the field with micron-size resolution and can sample hundreds of plants per cycle. This system is based on UHV's low cost linear x-ray tube technology and sophisticated reconstruction and image segmentation algorithms.

Slick Sheet: Project
Lawrence Berkeley National Laboratory (LBNL) will develop a field-deployable instrument that can measure the distribution of carbon in soil using neutron scattering techniques. The system will use the Associated Particle Imaging (API) technique to determine the three-dimensional carbon distribution with a spatial resolution on the order of several centimeters. A compact, portable neutron generator emits neutrons that excite carbon and other nuclei.

Slick Sheet: Project
Texas A&M AgriLife Research will develop low field magnetic resonance imaging (LF-MRI) instrumentation that can image intact soil-root systems. The system will measure root biomass, architecture, 3D mass distribution, and growth rate, and could be used for selection of ideal plant characteristics based on these root metrics. It will also have the ability to three-dimensionally image soil water content, a key property that drives root growth and exploration.

Slick Sheet: Project
Iowa State University (ISU) will develop new sensors that measure the amount of nitrogen in soils and plants multiple times per day throughout the growing season. Nitrogen fertilizer is the largest energy input to U.S. corn production. However, its use is inefficient due to a lack of low-cost, effective nitrogen sensors. Year-to-year variation in nitrogen mineralization, due to differences in soil water and temperature, creates tremendous uncertainty about the proper fertilizer input and can cause farmers to over-apply.

Slick Sheet: Project
Sandia National Laboratories will develop novel, field-deployable sensor technologies for monitoring soil, root, and plant systems. First, the team will develop microneedles similar and shape and function to hypodermic needles used in transdermal drug delivery and wearable sensors. The minimally invasive needles will be used to report on sugar concentrations and water stress in leaves, stems, and large roots in real-time.

Slick Sheet: Project
Colorado State University (CSU) will develop a high-throughput ground-based robotic platform that will characterize a plant’s root system and the surrounding soil chemistry to better understand how plants cycle carbon and nitrogen in soil. CSU’s robotic platform will use a suite of sensor technologies to investigate crop genetic-environment interaction and generate data to improve models of chemical cycling of soil carbon and nitrogen in agricultural environments.

Slick Sheet: Project
Lawrence Berkeley National Laboratory (LBNL) will develop an imaging-modeling toolbox to aid in the development of more efficient crops at field scales. The approach is based on a root phenotyping method called Tomographic Electrical Rhizosphere Imaging (TERI). TERI works by applying a small electrical signal to a plant, then measuring the impedance responses through the roots and correlating those responses to root and soil properties. Key target traits of the LBNL project include root mass, root surface area, rooting depth, root distribution in soil, and soil moisture content and texture.