Finding a proper location for a bee apiary is a crucial task for beekeepers and especially for travelling beekeepers. Normally beekeepers choose an appropriate apiary location based on their previous experience and sometimes the location may not be optimal for the bee colonies. This can be explained by different flowering periods, variation of resources at the known fields, as well as other factors. In addition it is very challenging to evaluate how many bee colonies should be placed in one geographical location for an optimal nectar foraging process. This research presents a model for finding the number of honey bee colonies needed for the optimal foraging process in the specific location, taking into account several assumptions. Authors propose to take into account potential field productivity, possible chemical contamination, surroundings of the apiary. To run the model, several steps have to be completed, starting from the selection of area of interest, conversion to polygons for further calculations, defining the roads in the selected area. The outcome of the model number of colonies that should be placed is presented to the user. The Python language was used for the model development. The model can be extended to use additional factors and values to increase the precision of the evaluation. In addition, input from users (farmers, agricultural specialists, etc.) about external factors that can affect the number of bee colonies in the apiary can be taken into account. This work is conducted within the Horizon 2020 FET project HIVEOPOLIS (Nr.824069).
The monitoring and predictions of various multilevel 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.
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