Estimating the Number of Drones at the Entrance of a Honey Bee Hive using Machine Learning Tools

There are three types of bee in a honey bee hive: one queen which is in charge of laying eggs and controlling the population of the hive, a large number of worker bees that are all female and hold various responsibilities to maintain the entire hive, and a small number of drones (about 2-3%) that are male and their sole job is to mate with queens and spread the genetics of their hive in rare successful mating flights. There are various reasons for having a larger than normal number of drones which may cause a hive to collapse. Among the reasons for having too many drones are: a bad queen or a worker bee laying unfertilized eggs. It is important that the number of drones be monitored carefully as that can provide a good indication of the health of a hive. In order to monitor the number of drones, we use a hive monitoring system called Beemon that is created in the Visual and Image Processing (VIP) lab in our department. This system allows beekeepers and researchers to monitor their hives and will aid them in detecting potential problems within their hives. Deviations from a normal pattern can signify problems within the hive that are preventable by immediate action. In this paper, we will provide the technical details on a computer vision program aimed at estimating the number of drone bees in videos that are taken in front of several honey bee hives. The program estimates the number of drones to assist the beekeepers learn about the possible deviations as they occur. This program utilizes Python and OpenCV to classify bees in videos by applying motion detection and background subtraction methods. Using the two methods in conjunction aids in suppressing error in bee detection in cases in which the hive has a high amount of traffic. This paper will share some of the early results for several hives.