Automatic detection and decoding of honey bee waggle dances

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° (± 7.37°), 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.

Forage area estimation in European honeybees (Apis mellifera) by automatic waggle decoding of videos using a generic camcorder in field apiaries

“…The information derived from decoding this dance can be used to estimate forage area . However, manual decoding protocols are labor-intensive both in real time and with digital video recordings (Wario et al, 2017). To address this issue, recent studies have used advances in computer technology to detect waggle dances from video data automatically (Kimura et al, 2011;Wario et al, 2015Wario et al, , 2017.…”

“…However, manual decoding protocols are labor-intensive both in real time and with digital video recordings (Wario et al, 2017). To address this issue, recent studies have used advances in computer technology to detect waggle dances from video data automatically (Kimura et al, 2011;Wario et al, 2015Wario et al, , 2017. Yet most such techniques require a laboratory environment equipped with artificial lighting or a relatively high-performance camcorder recording at a high frame rate.…”

“…However, these methods are not yet capable of decoding waggle dances automatically. Wario et al (2017) proposed a method for detecting waggle runs without trajectory monitoring of labeled bees, which would be a practical and timesaving innovation for estimating forage area. In this study, we adopt the same approach (Wario et al, 2017), which focus on detecting waggle runs through framewise pixel-based image analyses to decode run duration (corresponding to the distance from hive to forage site) and vertical orientation (corresponding to the bearing to a forage site relative to the sun's azimuth).…”

“…Wario et al (2017) proposed a method for detecting waggle runs without trajectory monitoring of labeled bees, which would be a practical and timesaving innovation for estimating forage area. In this study, we adopt the same approach (Wario et al, 2017), which focus on detecting waggle runs through framewise pixel-based image analyses to decode run duration (corresponding to the distance from hive to forage site) and vertical orientation (corresponding to the bearing to a forage site relative to the sun's azimuth). Since the purpose of our proposed method is to analyze video recordings from field apiaries, we assumed the use of generic camcorders and developed an analytical framework to detect the typical characteristics of the abdominal waggling movement using less sophisticated equipment than in similar studies such as Wario et al (2017).…”

“…In this study, we adopt the same approach (Wario et al, 2017), which focus on detecting waggle runs through framewise pixel-based image analyses to decode run duration (corresponding to the distance from hive to forage site) and vertical orientation (corresponding to the bearing to a forage site relative to the sun's azimuth). Since the purpose of our proposed method is to analyze video recordings from field apiaries, we assumed the use of generic camcorders and developed an analytical framework to detect the typical characteristics of the abdominal waggling movement using less sophisticated equipment than in similar studies such as Wario et al (2017). The performance of our automatic waggle run decoding procedure was evaluated by comparing forage site estimation maps derived from automatically and manually decoded waggle dance data.…”