SBIR-STTR Award

The project's goal is to enable machine learning ("AI") enabled pollinator ID and behavior detectionon agricultural producer lands where detection units send insect IDs and behavior counts back tothe producer. Our objective is to build on an existing USDA open-source automated insectidentification device that can already send machine vision results back to the farmer.All camera-based computer vision devices have limited usefulness in the field due to the veryheavy power consumption of cameras. To get past this barrier our primary technical objective is touse new LiDAR on a chip technology to reduce and sometimes eliminate the need to use devicecameras when detecting pollinators and recording pollination behavior events.The plan to accomplish this goal is to integrate the LiDAR chip onto the existing open-sourceboard. The LiDAR can provide data-rich information to the device without turning on a camera ifwe develop specialized software detection models and hardware. We will develop an "insect onthe end of a wire" test fixture to mimic flight and pollination patterns of live insects with deadspecimens. Using that fixture we will conduct thousands of identical trials to check our LiDARintegration success. Then we will use field trials with a rented bee hive to verify the value of thistechnology to insect detection and pollination even recording.This work aligns with section 8.13.3 a through c of the RFA enabling measurement of nativepollinator abundance and behavior mapping pollinator deficits and artificial systems for improvingpollination outcomes.