Estimating Dynamics of Honeybee Population Densities with Machine Learning Algorithms

Ziad Salem, Gerald Radspieler, Karlo Griparic, Thomas Schmickl
Machine Learning, Optimization, and Big Data. MOD 2017. LNCS. vol. 10710. Springer, Cham. 10710 (2018), 309-321

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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