Farmers should benefit greatly from digital twin technology, which leverages significant advances in big data, computing power and artificial intelligence to generate virtual representations of the physical world.
A digital twin is a virtual model designed to accurately reflect a physical object, process or system.
At least two UF/IFAS scientists are already using digital twin technology frequently in their research. One utilizes it to simulate out-of-season strawberry growth; the other to train a mobile robot to navigate and analyze vegetable crops for their traits – a process called “phenotyping.”
Dana Choi, a UF/IFAS assistant professor of agricultural and biological engineering at the Gulf Coast Research and Education Center (GCREC), utilizes the technology to simulate robotics and sensor tests for agricultural automation.
Here’s a snapshot of Choi’s approach:
- Virtual prototyping: Before deploying a robot in the field, she creates a digital twin to test the robot’s efficiency, mobility and functionality in a simulated environment. This helps her identify potential issues — such as mechanical failure, design errors and environmental challenges — and rectify them without incurring real-world costs or damages.
- Sensor simulation: She can test sensors’ performance in simulated environmental conditions, ensuring their reliability and accuracy. The sensors are used for crop monitoring, vehicle navigation and safety monitoring.
- Scenario testing: Through simulation, Choi can introduce varied conditions – like obstacles in the field, differing terrain styles or unexpected lighting situations — to understand how her robotic solutions would respond.
- Data-driven decision making: The data from these simulations helps scientists make decisions, whether it’s about refining a robot’s design or strategizing deployment for optimal results.
On the main UF campus in Gainesville, Charlie Li, a UF/IFAS professor of agricultural and biological engineering, utilizes digital twin technology to train a machine-learning model on a mobile robot for crop phenotyping and navigation in the field.
His group developed a digital twin framework for image augmentation to improve crop detection from complex and variable farm backgrounds.
“The concept involves ensuring that the physical robot and its digital twin closely imitate each other,” Li said. “This approach aims to reduce the discrepancies between simulated and real-world robotic crop phenotyping, like detecting crops and rows. By doing so, it minimizes the reality gap encountered when applying crop-detection models trained in simulation to real-world scenarios, without the need for additional real-world training.”
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ABOUT UF/IFAS
The mission of the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) is to develop knowledge relevant to agricultural, human and natural resources and to make that knowledge available to sustain and enhance the quality of human life. With more than a dozen research facilities, 67 county Extension offices, and award-winning students and faculty in the UF College of Agricultural and Life Sciences, UF/IFAS brings science-based solutions to the state’s agricultural and natural resources industries, and all Florida residents.
About AI at UF
The University of Florida is making artificial intelligence the centerpiece of a major, long-term initiative that combines world-class research infrastructure, cutting-edge research and a transformational approach to curriculum. UF is committed to building the nation’s first AI University by offering artificial intelligence courses to all students in its 16 colleges through our AI Across the Curriculum program. Our faculty and students can analyze vast amounts of data that can impact our country’s biggest challenges with HiPerGator, the most powerful, university-owned supercomputer in the nation.
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