Alfalfa bloom prediction using plant images and processing techniques for the purpose of improving pollination using alfalfa leafcutting bees

The purpose of this proposal is to conduct research that will provide alfalfa seed growers a bloom forecast application to improve their pollination practices and increase yield. Most U.S. growers outside of California use Alfalfa Leafcutting Bees (ALB) for pollination because they increase yield by more than 50% over honey bees. Growers spend ~$400/acre for ALB's, yet our customer diligence indicates that a further 250 lbs/acre yield is lost as they are unable to respond adequately to changing bloom conditions in the field.Using test plots provided by our commercial partner and an USDA research lab, we will develop a heuristic for alfalfa bloom prediction using imaging techniques to track the stages of plant development and comparing spectral images with manual observation. Ground sensors will be deployed to calculate the Growing Degree Days and photoperiod needed through the development progression to build predictive models for bloom onset using bud development markers as a basis. Sensors would also track weather, weed, and pest infestations to provide better control windows for bee release and to localize and reduce pesticide use.When commercialized, growers will use a smartphone application to receive a reliable forecast for bloom onset. We estimate we can double the decision window for growers to make the two most important pollination decisions; 1) bee release timing (incubation delay) and 2) pesticide application, resulting in significantly increased yield and revenue while simultaneously improving bee health.