BeeBr: uma proposta de arquitetura computacional na apicultura, para a predição de problemas na colmeia

Abstract

Brazilian beekeeping is a segment formed mainly by small families, in which the activity is a supplement to income. The sector has obsolete working methods, using in their entirety exercises manually and without any technological intervention; as a consequence, it leads to an exhausting process for the beekeeper, as well as negative effects on the health of the bees. Given this scenario, this study proposes to develop a model of computational architecture, in which it aims to contribute to beekeeping, minimizing interventions with the hives and thus ensuring the well-being of insects. Contrary to other research, it is intended to expose, from the point of view of scientific contribution, the elaboration and training of a new model of machine learning, which aims at the prediction of swarming, as well as exposing energy-efficient and sustainable solutions in terms of energy consumption of IoT equipment. On the technological side, BeeBr offers a complete low-cost solution for the beekeeping segment. As a result, BeeBr enabled readings of six hive variables for a period of 20 days. Through the collected data, a statistical analysis and the design of three experiments were allowed for the evaluation of the modern machine learning model; in final numbers, it was possible to reach values above 93% of hits in the swarm prediction and gains of 16.67% in relation to energy efficiency.