SIU Builds Field Robot and AI System to Detect Soybean Disease Before Symptoms Appear

Researchers at Southern Illinois University Carbondale are developing a field robot and artificial-intelligence system designed to identify soybean disease before symptoms become obvious to farmers.

The project is led by precision agriculture assistant professor Billy Ram and doctoral researcher Samuel Singh. Their robot uses multiple cameras, GPS and autonomous row-following capability to collect close-range imagery from soybean plants.

The team moved toward a ground robot after drone imagery proved insufficient for detecting lesions on the underside of leaves and near plant stems. That is an important limitation because some diseases can significantly reduce yield before symptoms are easy to see from above.

The current research focuses on frogeye leaf spot. The goal is to develop AI models that can identify disease hotspots and create maps that could support site-specific fungicide treatment rather than uniform spraying across an entire field.

The researchers say the longer-term objective is for machinery companies to adapt the technology into tractor or sprayer attachments that are practical and affordable for commercial farms.

The system remains a research project rather than a finished commercial product. Its value will depend on field validation across varieties, disease pressure, lighting conditions and production environments, but it illustrates how robotics and computer vision are moving deeper into crop scouting.