Scholarly record

APPLYING MACHINE LEARNING AGAINST BEEHIVES DATASET.

Kristina Dineva

Abstract

With introduction of new and advanced technologies in beekeeping it is possible to design and use Internet of Things (IoT) sensor systems embedded in beehives. This provides the ability to collect heterogeneous data and to provide real-time remote monitoring. Complex prognostic models are applied after the data is manipulated and normalized. The obtained results are used to predict the processes occurring in beehives. For this purpose, the collected data must be divided into at least two parts for training and validation of the constructed model. Such approach is widely used in the field of modern machine learning. Applying it to datasets from beehives allows new patterns to be detected between the processes in the hive and the parameters of the surrounding environment such as temperature, humidity, atmospheric pressure, CO2 and others. The purpose of this article is to describe the preparation and processing of IoT heterogeneous data collected from beehives and the application of the appropriate machine learning algorithms against it.

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DOI resolver: doi.org/10.5593/sgem2018/6.2/s25.005

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