Automatic detection and decoding of honey bee waggle dances

TL;DR

This paper introduces an automated system for real-time detection, decoding, and mapping of honey bee waggle dances, significantly improving efficiency over manual methods and enabling large-scale behavioral studies.

Contribution

The authors developed the first publicly available system that automatically detects, decodes, and maps honey bee waggle dances with high accuracy, facilitating advanced research.

Findings

Detection accuracy of 90.07%

Average decoding error of -2.92° (±7.37°)

System enables large-scale behavioral analysis

Abstract

The waggle dance is one of the most popular examples of animal communication. Forager bees direct their nestmates to profitable resources via a complex motor display. Essentially, the dance encodes the polar coordinates to the resource in the field. Unemployed foragers follow the dancer's movements and then search for the advertised spots in the field. Throughout the last decades, biologists have employed different techniques to measure key characteristics of the waggle dance and decode the information it conveys. Early techniques involved the use of protractors and stopwatches to measure the dance orientation and duration directly from the observation hive. Recent approaches employ digital video recordings and manual measurements on screen. However, manual approaches are very time-consuming. Most studies, therefore, regard only small numbers of animals in short periods of time. We have developed a system capable of automatically detecting, decoding and mapping communication dances in real-time. In this paper, we describe our recording setup, the image processing steps performed for dance detection and decoding and an algorithm to map dances to the field. The proposed system performs with a detection accuracy of 90.07\%. The decoded waggle orientation has an average error of -2.92{\deg} ($\pm$ 7.37{\deg} ), well within the range of human error. To evaluate and exemplify the system's performance, a group of bees was trained to an artificial feeder, and all dances in the colony were automatically detected, decoded and mapped. The system presented here is the first of this kind made publicly available, including source code and hardware specifications. We hope this will foster quantitative analyses of the honey bee waggle dance.