“Because the computer-simulated field never goes out of season, new berry-spotting tools can be prototyped even in the summer – speeding innovation,” said Choi.
Traditionally, training such systems required thousands of real images, each manually labelled—a time-consuming and expensive process. The digital twin eliminates that need, allowing synthetic images to be created and labelled instantly.
Beyond fruit detection, this technology supports development of smart sprayers, robotic pickers, and operator training—all without field access. It significantly reduces costs and boosts efficiency in agriculture technology innovation.
Florida’s strawberry industry is worth $500 million, with national production reaching $2 billion. This breakthrough helps reduce production costs while addressing labour challenges and improving overall efficiency.
With digital twin tools, researchers can now bring robotic solutions to market faster, helping farmers use advanced tools at lower costs and with greater precision.