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Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms

Part of the Lecture Notes in Computer Science book series (LNISA,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

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  • DOI: 10.1007/978-3-319-72926-8_26
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    A sample dataset is available at


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

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Correspondence to Ziad Salem .

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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.

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