An Autonomic, Adaptive and High-Precision Statistical Model to Determine Bee Colonies Well-Being Scenarios
Honey bees, important pollinators, are threatened by a variety of pests, pathogens and extreme climatic events, such as the winter period. This paper proposes a two-stages model that seeks to define and predict evolutionary scenarios for improving the bee colonies’ well-being. The used dataset has data from both internal and external beehive sensors, and on-site inspection of beekeepers from six apiaries between the years 2016-2018. In the first stage, three evolutionary scenarios were obtained (pessimistic, conservative and optimistic) through the clustering technique. In the second one, aiming to classify these scenarios, an elastic net penalty logistic regression model was obtained with an accuracy of ~99.5%.
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