Our researchers are on the forefront of technology and data science, harnessing the power of big data, supercomputers, and machine learning to advance decision-making in agriculture, robotics, health, conservation, and more.
These impacts are made possible through public and private investments, legislator support, multi-institutional partnerships, and the dedication of faculty and student scholars.
Below, we showcase a fraction of our world-class research in the area of data and technology. You can also subscribe to one of our ACES e-newsletters to stay abreast of new developments in ACES research.
Chris Harbourt (right) explains the application of drone technology for agriculture
The Center for Digital Agriculture, launched in late 2019, aims to sustainably feed a growing global population by harnessing the power of digital technology. The center, which brings together Illinois agronomists, economists, engineers, and data scientists, along with producers and industry partners, focuses on deploying automation, processing big data, improving crops and livestock, and integrating the human dimension of agriculture.
More than a dozen interdisciplinary projects are currently underway, including mapping crop photosynthetic capacity using airborne remote sensing; evaluating the effects of agricultural chemicals and other pollutants on rural populations; using machine learning to detect adulterated fertilizer in developing countries; and more.
A central part of the center's mission is sharing its research insights and approaches with future leaders, industry partners, and the public. For example, the center will serve as a hub for two first-of-their-kind undergraduate majors: computer science + crop sciences and computer science + animal sciences. A master of science in digital agriculture is currently being developed.
The center is also partnering with University of Illinois Extension to ensure that rural locations, a highly dispersed workforce, and variable local conditions do not limit, but actually enhance and can benefit from the impact of digital technologies.
Through its commitment to interdisciplinary, paradigm-shattering innovation, the center promises to emerge as the national and global leader in the digital agriculture revolution.
Funding: University of Illinois Provost’s Office, Investment for Growth grant ($2 million)
The College of Agricultural, Consumer and Environmental Sciences
The Grainger College of Engineering
The Carl R. Woese Institute for Genomic Biology
The National Center for Supercomputing Applications
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Researchers Justina Zurauskiene (left) and Zeynep Madak-Erdogan. Photo by Jillian Nickell.
Gestational diabetes in pregnant women can lead to health complications for both the mother and baby. Tests to detect the disease, typically performed in the second trimester, have shown disparities in accurate detection across different ethnicities.
Researchers at the University of Illinois, particularly in the area of molecular and computational biology, are working to improve detection of gestational diabetes by developing technology to identify universal biomarkers in blood to catch the disease earlier, regardless of factors such as racial background and geographical location.
As a first step, the researchers must develop a pipeline for collecting and analyzing the complex data sets involved. Once these algorithms are developed, supercomputers and state-of the-art data analysis at the National Center for Supercomputing Applications (NCSA) will aid the researchers in identifying the best biomarkers for early detection.
The new research is part of the Birmingham-Illinois Partnership for Discovery, Engagement, and Education (BRIDGE) project.
The approach builds on a previous project that looked at blood biomarkers related to breast cancer, environmental toxicants, and breast cancer disparities in Southside Chicago. Zeynep Madak-Erdogan, an ACES researcher and NCSA fellow, now working with the BRIDGE project, worked with NCSA researchers to develop algorithms through machine learning applications, to identify blood biomarkers that would predict an individual’s risk for cancer due to toxicant exposure.
Funding: The Birmingham-Illinois Partnership for Discovery, Engagement, and Education (BRIDGE); College of Agricultural, Consumer and Environmental Sciences Division of Nutritional Sciences Vision 20/20 grant, ACES Future Interdisciplinary Research Explorations (FIRE) grant; United States Department of Agriculture National Institute of Food and Agriculture; and National Center for Supercomputing Applications.
ACES investigator and departments
Zeynep Madak-Erdogan, Food Science and Human Nutrition; National Center for Supercomputing Applications
Related news story
BRIDGE-ing the gap between diagnostics and gestational diabetes
Girish Chowdhary with TerraSentia
Traditional single-crop agriculture is cost- and labor-efficient but places considerable stain on the environment. Polycultures that focus on diverse, high-value crops promote sustainability and economic growth, but they are labor intensive. A solution may be small, adaptable robots that can perform a variety of agriculture-related tasks. The robots combine the speed and power of technology with attention to detail that would otherwise be provided human labor.
Researchers at the University of Illinois have developed TerraSentia, an unmanned ground vehicle (UGV) that moves under the crop canopy where tractors and large equipment cannot reach. The small, nimble robots are semiautomatic and directed by a mobile app. They can work together as a team, moving up and down crop rows and performing multiple tasks.
The TerraSentia robots were developed to collect crop data such as plant count, stem height, and leaf measurements. Those crop scouting robots are being manufactured and sold by EarthSense, a start-up company that’s incubated at the University of Illinois Research Park. Other potential applications, including weed management and fruit picking, are in the testing and development stages.
TerraSentia robots intended for weed control are equipped with small, mechanical arms that pull weeds as they move down the rows of crops. Machine learning techniques teach the robots to distinguish between weeds and crops. Weeding robots can benefit organic growers, as well as conventional producers who struggle with superweeds that are resistant to herbicides.
The researchers are also developing robots that can be applied in agroforestry for berry and fruit picking. That requires soft, flexible robotic arms that can reach fruit at different plant heights.
Crop robots with multiple applications can play a key role in profitable and practical sustainable agricultural systems that contribute meaningfully to global food security, the researchers say.
Funding: U.S. Department of Energy ($3.1 million); U.S. Department of Agriculture (USDA) and the National Science Foundation's National Robotics Initiative Program ($900,000); USDA National Institute of Food and Agriculture and the National Science Foundation's Cyber Physical Systems program ($885,000); the National Science Foundation's Small Business Innovation Research Award ($225,000).
ACES investigators and departments
Girish Chowdhary, Agricultural and Biological Engineering
Stephen Long, Crop Sciences, Plant Biology, Institute for Genomic Biology
Adam Davis, Crop Sciences
Carl Bernacchi, Crop Sciences, Plant Biology, USDA Agricultural Research Service
TerraSentia robots, Agricultural and Biological Engineering faculty featured in New York Times
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Teachable, ultra-compact, autonomous phenotyping robot introduced to investors, market
One of a fleet of nanosatellites providing continuous data for this research. Photo by Planet Lab.
Biofuels are considered an environmentally friendly alternative to petroleum-based fuels, yet the principal sources of ethanol in the U.S. are corn and soybeans. Depending on their management, these crops can produce significant greenhouse gases (GHGs), including nitrous oxide, carbon dioxide, and methane. But because of the vast scale of industrial agriculture and the many variables involved, scientists have not been able to arrive at a true estimate of the GHG emissions related to biofuel production.
A new University of Illinois project takes an innovative approach, integrating on-farm measurements with airborne and satellite imagery to provide a comprehensive view of GHG emissions from corn and soybeans. The project relies on a fleet of nanosatellites known as CubeSats that monitor the planet daily at 3-meter resolution. Researchers will also leverage the supercomputing power of the National Center for Supercomputing Applications and advanced GHG-tracking instrumentation installed on campus farms.
The research team will compare emissions from corn and soybean fields under common management practices relating to tillage, cover crops, and fertilizers. Corn and soybeans will also be compared to two second-generation perennial biofuel crops, switchgrass and miscanthus.
The team will make reliable, real-time data publicly available, with the goal of informing on-farm management practices that could reduce GHGs. This will require designing cyberinfrastructure that can manage and present highly complex data in a user-friendly format.
Funding: U.S. Department of Energy ($3.3 million)
Kaiyu Guan, Natural Resources and Environmental Sciences, National Center for Supercomputing Applications
D.K. Lee, Crop Sciences
Nanosatellites improve detection of early-season corn nitrogen stress
New research accurately predicts Australian wheat yield months before harvest
Satellites, supercomputers, and machine learning provide real-time crop type data