Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms

  • Ziad SalemEmail author
  • Gerald Radspieler
  • Karlo Griparić
  • Thomas Schmickl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)


The estimation of the density of a population of behaviourally diverse agents based on limited sensor data is a challenging task. We employed different machine learning algorithms and assessed their suitability for solving the task of finding the approximate number of honeybees in a circular arena based on data from an autonomous stationary robot’s short range proximity sensors that can only detect a small proportion of a group of bees at any given time. We investigate the application of different machine learning algorithms to classify datasets of pre-processed, highly variable sensor data. We present a new method for the estimation of the density of bees in an arena based on a set of rules generated by the algorithms and demonstrate that the algorithm can classify the density with good accuracy. This enabled us to create a robot society that is able to develop communication channels (heat, vibration and airflow stimuli) to an animal society (honeybees) on its own.


Machine learning Data mining Classification algorithms Density estimation Robots Honeybees 



This study was supported by the EU FP7 FET-Proactive project \(ASSISI_{bf}\), grant no. 601074.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ziad Salem
    • 1
    Email author
  • Gerald Radspieler
    • 1
  • Karlo Griparić
    • 2
  • Thomas Schmickl
    • 1
  1. 1.Artificial Life Lab at the Institute for ZoologyKarl-Franzens-University GrazGrazAustria
  2. 2.LARICS Lab at the Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

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