Modeling bee hive dynamics. Assessing colony health using hive weight and environmental parameters

Abstract

Our state-of-the-art methods study hive weight and predict bee health and colony condition using advanced machine learning tools trained with unlabeled data. By integrating methodologies such as signal extraction, similar trend monitoring, principal component analysis, and MM-Regression, our goal is to translate hive weight fluctuations into predictive insights for future hive monitoring systems. In particular, signal extraction methods are used to obtain an interpretable signal and to detect level shifts, exploratory analysis is used to visually detect dissimilar weight trajectories from nearby hives, historical data are used to robustly predict hive weights, and to trigger an alarm when predictions and actual observations differ significantly. Our study shows how these methods can be successfully used to analyze and predict hive weights and underscore the need for future research to accumulate labeled data and to adopt a holistic perspective, incorporating a wider spectrum of influences of hive weight simultaneously.