DEVELOPING AUTOMATED TOOLS FOR DETECTION AND IDENTIFICATION OF INSECT POLLINATORS
Pollination is critical to many ecosystem services, such as the maintenance of biodiversity and human nutrition. Addressing how plant-pollinator interactions change across environmental gradients requires quantifying pollinator visitation.
However, we are limited by the available tools. Ecologists typically observe pollinators in the field, record the species identity of the easily distinguished individuals, and collect with a net the remaining individuals so that they can be later identified to species in the lab. This is labor and time-intensive.
The goal of this research is to develop an automated camera system capable of detecting and identifying insect pollinators to the lowest possible taxonomic level (order > family > genus > species). The purpose of the AI tools is to advance the field of pollination ecology by eliminating the time-consuming identification step that currently limits our ability to acquire data for broad-scale questions.