Drone Imaging for Invasive Species
This multi-year project used drones and machine learning to help detect and manage aquatic invasive species in the Great Lakes region. It was funded through the Great Lakes Restoration Initiative (GLRI) by the U.S. Fish and Wildlife Service (GLRI AISMP FY22; $799,794 total; PI: Sarah Whitney). VAR Lab received a $191,950 subaward, with Dr. Christopher R. Shelton serving as co-principal investigator.
From 2023 to 2025, VAR Lab defined the project’s data acquisition protocols, using 360-degree and 3D aerial imagery to capture plant growth patterns, landscape changes, and areas of concern that are difficult to see from the ground. Biological researchers and machine learning engineers used that imagery to train models that reached 97% classification accuracy. Senior design students joined the project later to help build out the system.
Drone-based imaging gave the team a wider view of each site, allowing them to document larger areas efficiently and examine how invasive species spread across a landscape. The workflow supports environmental research, land management, and education by creating visual records that help researchers and students understand ecological change over time.
