Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms

Ziad Salem, Gerald Radspieler, Karlo Griparic, Thomas Schmickl
The 3rd International Conference on Machine learning, Optimization & big Data – MOD 2017. LNCS (2017), 13

Abstract:

The estimation of the density of a population of behaviorally 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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