Application of Data Layering in Precision Beekeeping: The Concept

Abstract

The monitoring and predictions of various multi-level states of honeybee colonies are performed using emerging Internet of Things technologies and data processing methods. It is become common to use multiple sensors and devices providing multi-modal data to monitor a single activity. Modern data analysis and data processing procedures include a step of data fusion in order to provide more accurate input data. This, however, requires implementation of machine learning and large data sets, whereas gathering large data sets of real time and observation data is a common problem for small to medium size apiaries. This why there are no real implementation of data fusion method in precision beekeeping field. The aim of this paper was to introduce the concept of data layering, which aims to solve the global precision beekeeping problems without implementation of machine learning. The concept was demonstrated within the scope of foraging optimization problem using three data sets: flowering calendar data, rainfall precipitation data and bee activity data.