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International Workshop on Machine Learning, Optimization, and Big Data

MOD 2017: Machine Learning, Optimization, and Big Data pp 309–321Cite as

Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

Abstract

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.

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Notes

  1. 1.

    Available for download at http://www.cs.waikato.ac.nz/ml/weka/.

  2. 2.

    A sample dataset is available at https://doi.org/10.5281/zenodo.824923.

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Acknowledgments

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

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Authors and Affiliations

  1. Artificial Life Lab at the Institute for Zoology, Karl-Franzens-University Graz, Universitätsplatz 2, 8010, Graz, Austria

    Ziad Salem, Gerald Radspieler & Thomas Schmickl

  2. LARICS Lab at the Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000, Zagreb, Croatia

    Karlo Griparić

Authors
  1. Ziad Salem
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  2. Gerald Radspieler
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  3. Karlo Griparić
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  4. Thomas Schmickl
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Corresponding author

Correspondence to Ziad Salem .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Salem, Z., Radspieler, G., Griparić, K., Schmickl, T. (2018). Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_26

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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