Capped honey segmentation in honey combs based on deep learning approach

Honey is the food stored by honey bees for periods when it is scarce in the field as well as being a product that is consumed worldwide by humans. Each hive generates different amounts of honey depending on the population of the bee hive, health state or environmental factors. In fact, the reserves of honey provide beekeepers with a double function: to predict the amount of honey that can be obtained and to analyze the state of the bee colonies. The assessment of honey reserves is commonplace in scientific research related to the health of bee colonies, genetic improvement or environmental issues, and emerging technologies can provide useful tools to evaluate honey stored in hives. In this context, this work proposes a methodology to detect the honey areas in high resolution photographs automatically using methods based on deep learning. Specifically, the methodology follows a “divide and conquer” approach where the images are separated into tiles with overlapping areas that are used by a semantic segmentation algorithm based on Feature Pyramid Network (FPN), detecting the honey in each tile to finally merge the tiles back into the complete image. The proposal has been compared with different feature extractors (backbones) and other semantic segmentation models, obtaining on average accurate results above 90% and 87% in the Dice score and IOU metrics respectively.