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Transportation Energy Resources from Renewable Agriculture

The TERRA program is facilitating improvement of advanced biofuel crops, specifically energy sorghum, by developing and integrating cutting-edge remote sensing platforms, complex data analytics tools, and high-throughput plant breeding technologies. Project teams are constructing automated systems to accurately measure and analyze crop growth in the field, thoroughly characterizing genetic potential and creating algorithms for selecting the best plants to reproduce. These innovations will accelerate domestic production of sustainable, renewable, and affordable liquid transportation fuels. The program will also generate the world's largest public reference database of sorghum plant characteristics and genetic composition that will facilitate research and development efforts across public and private sector institutions and in other important agricultural crops.
For a detailed technical overview about this program, please click here. 

Clemson University

Breeding High Yielding Bioenergy Sorghum for the New Bioenergy Belt

Clemson University is partnering with Carnegie Mellon University (CMU), the Donald Danforth Plant Science Center, and Near Earth Autonomy to develop and operate an advanced plant phenotyping system, incorporating modeling and rapid prediction of plant performance to drive improved yield and compositional gains for energy sorghum. The team will plant and phenotype one of the largest sets of plant types in the TERRA program. Researchers will design and build two phenotyping platforms - an aerial sensor platform and a ground-based platform. The aerial platform, developed by Near Earth Autonomy, is a fast moving, autonomous helicopter outfitted with sensors that will collect image data from above. The ground platforms are customized robots from CMU that will drive between crop rows below the plant canopy and collect data using two distinct sensor suites. The first will use sophisticated cameras and imaging algorithms to develop detailed 3D models of individual plants and their canopy structure. The second will have the unique ability to directly contact the plant in order to systematically measure physical characteristics that were previously measured manually with labor-intensive, low-throughput methods. The team will use machine learning techniques to analyze the data gathered from the phenotyping systems and translate this into predictive algorithms for accelerated breeding of improved biofuel plants.

Donald Danforth Plant Science Center

A Reference Phenotyping System for Energy Sorghum

The Donald Danforth Plant Science Center, in collaboration with partners from seven institutions, proposes an integrated open-sourced phenotyping system for energy sorghum. Phenotyping is the assessment of observable plant traits, and is critical for breeding improvements. The team will develop a central repository for high quality phenotyping datasets, and make this resource available to other TERRA project groups and the wider community to stimulate further innovations. The team will collect data with their complete system that will include a number of components. First, the team will install, operate, and maintain a reference phenotyping field system that employs a bridge-like overhead structure with a moveable platform supporting sensing equipment, called the Scanalyzer, at the Maricopa Agricultural Center (MAC) at the University of Arizona. The Scanalyzer's advanced sensors will be used for automated high-throughput phenotyping to gather data from the energy sorghum in the field. Second, the project will combine field- and controlled-environment phenotyping. The controlled-environment facilities allow the team to more precisely manipulate environmental conditions and resolve complex dynamic interactions observed in the field. Third, plant and environment data gathered will be used to create computational solutions and predictive algorithms to improve the ability to predict phenotypes; increasing the ability to identify traits for improved biomass yield earlier in a plant's development. Collected data will also be used in the fourth component of the project, advancing our understanding of phenotype-to-genotype trait associations, determining which genes control observable traits in the sorghum. Some traits are largely determined by genes and others are largely determined by environmental factors; work in this project will help elucidate the differences. All of these components generate an incredible amount of data. An "Open Data" policy is central to the philosophy of the Danforth project. To ensure that this data is useful, the team will convene a standards committee selected in collaboration with the TERRA program to standardize phenotyping efforts between institutions. This sharing of standards, data, and open-source code will reduce redundancy, lower costs for researchers, allow for long-term curation, and unlock potential new innovations from entrepreneurs outside the TERRA community.

Pacific Northwest National Laboratory

The Consortium for Advanced Sorghum Phenomics (CASP)

Pacific Northwest National Laboratory (PNNL), along with its partners, will use aerial and ground-based platforms to identify traits required for greater production yield and resistance to drought and salinity stresses to accelerate sorghum breeding for biofuel production. The project will combine plant analysis in both outdoor field and indoor greenhouse environments as each provides unique advantages; and will use robotics and imaging platforms for increased speed and accuracy of data collection. Traditionally aboveground biomass is measured by harvesting, drying, and weighing the plant material. As an alternative approach, the team will develop non-destructive high-throughput methods to measure biomass over time. Drought tolerance will be measured by mapping water stress and using sensors to compare the difference between the canopy temperature and air temperature. The overall goal of the project is to understand the traits related to increasing biomass yield and drought/salinity stress, and to predict those traits in the early stages of plant development, before those traits become apparent using current methods.

Purdue University

Automated Sorghum Phenotyping and Trait Development Platform

Purdue University, along with IBM Research and international partners from the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia) will utilize remote sensing platforms to collect data and develop models for automated phenotyping and predictive plant growth. The team will create a system that combines data streams from ground and airborne mobile platforms for high-throughput automated field phenotyping. The team's custom "phenomobile" will be a mobile, ground-based platform that will carry a sensor package capable of measuring numerous plant traits in a large number of research plots in a single day. In addition, the team will use unmanned aerial vehicles (UAVs) equipped with advanced sensors configured to optimize the collection of diverse phenotypic data and complement the data collected from the phenomobile. Advanced image and signal processing methods will be utilized to extract phenotypic information and develop predictive models for plant growth and development. IBM Research will contribute high-performance computing platforms and advanced machine learning approaches to associate these measurements with genomic information to identify genes controlling sorghum performance. International partners from CSIRO will lend their expertise in crop modelling and phenotyping to the effort.

Texas A&M Agrilife Research

Automated TERRA Phenotyping System for Genetic Improvement of Energy Crops

Texas A&M University, along with Carnegie Melon University (CMU), will develop a rugged robotic system to measure characteristics of sorghum in the field. Traditionally this type of data collection is performed manually and often can only be collected when the crop is harvested. The team from CMU will create an automated gantry system with a plunging sensor arm to characterize individual plants in the field. The sensor arm of the gantry system allows the team to collect data not only from above, but to descend into the canopy and take measurements within. The team will utilize machine learning algorithms to interpret the field data and correlate them to plant phenotypes, molecular markers, and genes of interest linked to the field phenotypes. TAMU will incorporate this technology into its world class sorghum breeding program to increase the rate of genetic improvement.

University of Illinois, Urbana Champaign

TERRA MEPP (Mobile Energy-crop Phenotyping Platform)

The University of Illinois, Urbana-Champaign (UIUC) with partners, Cornell University and Signetron Inc., will develop a small semi-autonomous, ground-based vehicle called TERRA-MEPP (Mobile Energy-Crop Phenotyping Platform). The platform performs high-throughput field-based data collection for bioenergy crops, providing on-the-go measurements of the physical structure of individual plants. TERRA-MEPP will use visual, thermal, and multi-spectral sensors to collect data and create 3-D reconstructions of individual plants. Newly developed software will interpret the data and a model-based data synthesis system will enable breeders to select the most promising sorghum lines for bioenergy production much sooner than currently possible, dramatically increasing the rate of genetic advancements in biomass.

ARPA-E’s Transportation Energy Resources from Renewable Agriculture (TERRA) program is bringing together top experts from different disciplines – agriculture, robotics and data analytics – to rethink the production of advanced biofuel crops. ARPA-E Program Director Dr. Joe Cornelius discusses the TERRA program and explains how ARPA-E’s model enables multidisciplinary collaboration among diverse communities. The video focuses on two TERRA projects—Donald Danforth Center and Purdue University—that are developing and integrating cutting-edge remote sensing platforms, complex data analytics tools and plant breeding technologies to tackle the challenge of sustainably increasing biofuel stocks.
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