A new method for real-time tracking of pollinators – quantifying honeybee and native bee activity

At Atolla Tech we are proposing a new method for assessing the pollination efficiency of differentbee species in real-time thereby aiding growers in optimizing pollination of their crop whether theyrely on wild pollinators or use honey bee services or both. Our technology includes a lidar andmachine learning (ML) algorithm that detects identifies and tracks pollinator species in real-time.Their flight activity will be viewable on a live map and a Pollination Visitation Index (PVI) levelwill be provided at the end of the day. The PVI will provide the grower with a summarized activitylevel that will be represented as a value between 0 and 1 where 0 represents no pollination activityand 1 represents high pollination activity. The PVI model will be developed under the SBIR phase Iproposal and then analyzed at the end of the season. Automating the pollination efficiency/successusing a remote sensing in real-time approach will allow for optimization in pollination practices. Itopens the possibility for quantifying wild bee and honey bee levels in real-time thereby optimizingthe timing duration and placement of honey bee hives each season. In this trial it is our goal tounderstand how much of a need there is for honey bees in Southeast blueberry.Specificallywhether placing honey bee colonies in blueberry orchards is worth the investment or are nativelocal pollinators (e.g. southeastern blueberry bee) that are adapted to blueberry sufficient.