Atolla Tech, Inc.

At Atolla Tech We Are Proposing a New Method for Assessing the Pollination Efficiency of Different Bee Species in Real-time Thereby Aiding Growers in Optimizing Pollination of Their Crop Whether They Rely on Wild Pollinators or Use Honey Bee Services or Both. Our Technology Includes a Lidar and Machine 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) Level Will Be Provided at the End of the Day. the Pvi Will Provide the Grower With a Summarized Activity Level That Will Be Represented as a Value Between 0 and 1, Where 0 Represents No Pollination Activity and 1 Represents High Pollination Activity. the Pvi Model Will Be Developed Under the Sbir Phase I Proposal and Then Analyzed at the End of the Season. Automating the Pollination Efficiency/success Using a Remote Sensing in Real-time Approach Will Allow for Optimization in Pollination Practices. It Opens the Possibility for Quantifying Wild Bee and Honey Bee Levels in Real-time, Thereby Optimizing the Timing, Duration, and Placement of Honey Bee Hives Each Season. in This Trial, It Is Our Goal to Understand How Much of a Need There Is for Honey Bees in Southeast Blueberry. Specifically, Whether Placing Honey Bee Colonies in Blueberry Orchards Is Worth the Investment or Are Native, Local Pollinators (e.g., Southeastern Blueberry Bee) That Are Adapted to Blueberry Sufficient.