Abstract
The widespread decline of honey bee (Apis mellifera L.) colonies registered in recent years has raised great attention to the need of gathering deeper knowledge about this phenomenon, by observing the colonies’ activity to identify possible causes, and design corresponding countermeasures. In fact, honey bees have well-known positive effects on both the environment and human life, and their preservation becomes critical not only for ecological reasons, but also for the social and economic development of rural communities. Smart sensor systems are being developed for real-time and long-term measurement of relevant parameters related to beehive conditions, such as the hive weight, sounds emitted by the bees, temperature, humidity, and CO inside the beehive, as well as weather conditions outside. This paper presents a multisensor platform designed to measure the aforementioned parameters from beehives deployed in the field, and shows how the fusion of different sensor measurements may provide insights on the status of the colony, its interaction with the surrounding environment, and the influence of climatic conditions.
Highlights
The role of honey bees (Apis mellifera L.) in the natural ecosystem and their importance for the health of the environment and life preservation are well known
Colony collapse disorder (CCD) is a recent, pervasive syndrome affecting honey bee colonies in Europe and the rest of the world, which is characterized by a sudden disappearance of honey bees from the hive [1,2,3]
It is evident in both cases a sudden weight variation between 3 and 4 kg that indicates a consistent part of the population of the hive is moving away. This fact has been correlated with the sound measurements, showing that the swarming event corresponds to an increase of the sound activity, as clearly visible in Figures 12b and 13b
Summary
The role of honey bees (Apis mellifera L.) in the natural ecosystem and their importance for the health of the environment and life preservation are well known. In [9], a method based on supervised machine learning approach that uses data from in-hive sensors (i.e., internal temperature and beehive weight), weather and apiary inspections to forecast the health status of honey bee colonies is presented. Starting from the state of the art related to monitoring systems and technological developments, this work extends the results presented in [22] and aims at developing a complete, multiparametric smart sensor-based measurement system capable of monitoring in real-time a colony of beehives, and discriminating several events, such as swarming event, beehive theft, honey gathering, reserve food lack, decrease of bees’ number due to illness.
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