STRIVING TO ENSURE POLLINATOR POPULATIONS REMAIN HEALTHY AND CAPABLE OF PROVIDING IMPORTANTPOLLINATION SERVICES REQUIRES RESEARCH BY THE SCIENTIFIC COMMUNITY AND APPLICATION BY THEAGRICULTURAL COMMUNITY, THE PUBLIC, AND CONSERVATIONISTS. RELIABLE IDENTIFICATION OF POLLINATORS,SUCH AS BEES, IS A CRITICAL TO MAINTAINING BEE HEALTH. HOWEVER, BECAUSE BEES CAN HAVE ONLY SUBTLEMORPHOLOGICAL DIFFERENCES, SPECIES-LEVEL IDENTIFICATION CAN BE DIFFICULT, REQUIRING SPECIALIZEDTAXONOMIC KNOWLEDGE. THIS RESULTS IN A BOTTLENECK THAT IS EXPENSIVE AND TIME CONSUMING, WHICHSLOWS THE PACE OF RESEARCH AND ADOPTION OF NEW APPLICATIONS. MOREOVER, SETBACKS FOR POLLINATORRESEARCH CAN RESULT FROM ERRORS BASED ON MISIDENTIFICATION IF EXPERTS ARE UNAVAILABLE OR IF FUNDS AREINSUFFICIENT. HOWEVER, NEW TECHNOLOGY THAT IS CURRENTLY BEING DEVELOPED IN THE FIELDS OF MACHINELEARNING AND COMPUTER VISION ARE ENABLING FAST AND RELIABLE AUTOMATED IDENTIFICATION OF OBJECTSFROM IMAGES. CUTTING-EDGE TECHNIQUES, SUCH AS CONVOLUTIONAL NEURAL NETWORKS (CNNS), ARE BEINGEMPLOYED IN DIVERSE FIELDS AND HELPING TO DRIVE ADVANCES IN PRECISION AGRICULTURE AND INSECTIDENTIFICATION. WE PROPOSE TO DEVELOP A LARGE IMAGE DATASET OF EXPERTLY IDENTIFIED BEES TO USE ASINPUT TO CNNS FOR AUTOMATED BEE IDENTIFICATION. WE WILL FOCUS ON (1) THE BUMBLE BEES (BOMBUS)OF NORTH AMERICA AND (2) THE BEES (ANTHOPHILA) OF KANSAS, USA, WHICH REPRESENT BEE SUBSETS OFINTEREST FOR ADDRESSING BEE HEALTH. WE WILL ALSO PRODUCE A MOBILE APP THAT WILL ALLOW NON-EXPERTSTO IDENTIFY BEES TO SPECIES FROM IMAGES. USING STATE-OF-THE-ART TECHNOLOGY, OUR PROJECT WILL THUS PUTA GREATLY NEEDED TOOL INTO THE HANDS OF THOSE ACTING TO MAINTAIN POLLINATOR HEALTH.