2025: AI-Enabled Robotic eDNA Sampling of Waterways
Climate change is rapidly altering freshwater ecosystems, yet early detection of species loss, range shifts, or invasive introductions remains difficult. Environmental DNA (eDNA) sampling offers unmatched sensitivity but is highly susceptible to contamination, undermining reliability and inflating costs. While autonomous robotic samplers excel at clean, consistent spatio-temporal sampling, they are prohibitively complex for field ecologists to operate and debug in the field. This project pairs autonomous unmanned surface vehicles (USVs) with an AI-driven conversational interface to enable clean, large-scale eDNA sampling by field ecologists, in turn boosting detection probabilities for emerging environmental problems.
Investigators: Kirstin Petersen, Cornell Engineering; Peter McIntyre, Cornell CALS