First-Ever Digital Twin of Corn Field Set to Transform Crop Research

James Schnable and colleagues to create digital twin of corn field through NSF grant

With a three-year grant of nearly $1 million from the National Science Foundation, James Schnable, a plant scientist at the University of Nebraska–Lincoln, is collaborating with researchers from Iowa State University and Purdue University to develop the world’s first digital twin of a corn field. The project, which totals $2 million in funding, will use advanced computational methods to create virtual representations of corn fields, allowing scientists to simulate various environmental and genetic factors to determine how corn crops perform under different conditions.

The concept of a digital twin is not new; it has been widely used in industries such as manufacturing, healthcare, and transportation, where digital models of physical systems or products can help predict outcomes and optimize performance. This technology is now being applied to agriculture, an area Schnable and his colleagues have been eyeing for years. The digital twin will revolutionize how scientists study corn by enabling simulations of different planting arrangements, hybrid varieties, and environmental conditions, all without needing to plant a single seed.

The aim of the digital twin is to help researchers bypass the constraints imposed by real-world field tests, where only a limited number of variables can be tested at a time. Schnable explained that the new technology will allow scientists to sift through millions of possible planting combinations to find those with the highest potential for success. “The more options we can explore, the faster we’ll be able to find the most promising solutions,” Schnable said.

Collaborative Effort

Schnable is collaborating with Baskar Ganapathysubramanian, a professor at Iowa State University who specializes in artificial intelligence, and Bedrich Benes, a Purdue University expert in 3D simulation and plant modeling. The trio’s complementary skills make the digital twin project possible, combining Schnable’s expertise in crop genetics, Ganapathysubramanian’s work in AI, and Benes’ knowledge of plant modeling. Together, they are breaking down long-standing barriers in agriculture, particularly around the time-consuming and labor-intensive process of field data collection.

The team will make use of Nebraska’s cutting-edge LemnaTec High-Throughput Plant Phenotyping System, housed at Nebraska Innovation Campus, to gather detailed, high-resolution data on corn plants throughout their life cycle. Benes will use this data to create the digital twin, while Ganapathysubramanian will apply innovative ray tracing methods to simulate how light is distributed throughout the canopy, helping to optimize planting strategies. According to Schnable, these advances in computational techniques are what make the project feasible today. “It’s only with these advances in AI and high-performance computing that we’re able to do this now, something that wasn’t possible before,” he said.

Real-World Applications

Once the digital twin is up and running, it will allow the research team to determine the most effective planting arrangements to maximize light absorption, minimize water loss, and increase yield. It will also help identify the ideal genetic traits for corn plants, such as leaf shape and arrangement, that are best suited for specific environmental conditions.

Using inverse procedural modeling and quantitative genetics, Schnable will work to pinpoint the genes responsible for these traits, which can then inform the development of new corn hybrids designed to thrive in various conditions. “We’ll be able to not only model the best physical traits but also plan for how to breed corn with those traits,” Schnable explained. The goal is to create corn varieties that are both highly productive and resource-efficient, helping to address global food security challenges.

Nebraska’s Role in Agricultural Innovation

This project places the University of Nebraska–Lincoln at the forefront of agricultural innovation. The institution’s strengths in plant phenotyping and corn genetics make it an ideal base for a project of this magnitude. Schnable, who has been with the university since 2014, said the work represents a return to his roots in computational biology. While he has spent much of his time conducting field experiments in recent years, he is eager to get back to the fast-paced work of digital simulations.

For Schnable and his colleagues, the digital twin project marks an important step toward leveraging digital technologies to improve agricultural outcomes. As Schnable put it, “Being able to simulate, tweak, and test planting strategies in a virtual environment, and see the results immediately, is incredibly exciting.”

This research promises to accelerate the development of more resilient and efficient corn crops, offering a new tool for addressing the global challenge of food security in a changing climate.

Sources: National Science Foundation

News Release Contact(s)

James Schnable, Associate Professor of Agronomy and Horticulture, T: 402-472-4540