Honey bee colony assessment is usually carried out via the laborious manual task of counting and classifying comb cells. Beekeepers perform this task many times throughout the year to asses the colony’s strength and to track its development. As you can imagine, this is an extremely time-consuming and error-prone task. We will share our experience with the development of a tool for automatic honeybee colony assessment, the DeepBee. 

DeepBee is a tool that encapsulates an image classification pipeline using classical image processing methods and state-of-the-art Deep Neural Networks (DNN) for image segmentation and classification. To get to the final solution, we have compared 13 distinct DNN architectures and chosen the best model based on several metrics. We discuss the steps taken from image collection to the delivery of the final solution, highlighting the mistakes we have done during the process, the hurdles we overtook, and the lessons learned.