Project Grant 2551220

The National Science Foundation Division of Computer and Network Systems awarded South Dakota State University one million dollars on September 1, 2026, under the Computer and Information Science and Engineering program (CFDA 47.070) to develop physics-guided latent space models for detecting occluded objects in agricultural settings.

The project, titled "Hidden Objects: Physics-Guided Latent Space Models for Detecting Occluded Objects," addresses the challenge of robotic and camera-based systems failing to detect partially hidden objects such as ripe fruit obscured by leaves and branches. The research team will develop a thermal sensing approach that applies controlled heat pulses to plant regions and uses thermal cameras to observe cooling patterns over time. Because fruit, leaves, stems, and surrounding environments absorb and release heat differently, their unique thermal signatures provide information unavailable from standard imagery. The project integrates multimodal sensing hardware—synchronized thermal, color, depth, and near-infrared data—with physics-guided machine learning algorithms and a robotic test platform. By analyzing temporal thermal evolution following heat pulses and applying physical principles of heat diffusion and cooling to guide machine learning, the approach aims to improve detection robustness to occlusion, lighting changes, and variable outdoor conditions.

The award runs from September 1, 2026, through August 31, 2030. Performance occurs in Brookings, South Dakota. The assistance type is a Project Grant.