A cluster-classification method for accurate mining of seasonal honey bee patterns

Abstract Bees are the main pollinators of most wild plant species and insect-pollinated crops and are essential for both plant ecosystems maintenance and humans food production. Among the crops used for human feeding, 75% depend on pollination. In addition to the fact that uncertainty around the beekeeping activity could jeopardize the economic value of pollination, data on honey bee colony losses exist but have not been thoroughly and systematically analyzed to identify potential causal factors. Recognition of seasonal honey bee data patterns can be useful for a number of purposes such as swarming observations, and for forecasting colonies absconding - especially for those hives where the resources are scarce. Here we propose a method to identify honey bee seasonal patterns. The main aim of this research in identifying these patterns is to assist the beekeeper in the management and maintenance of their hives, and, additionally, to prove that with machine learning and, in particular, unsupervised learning is possible to detect seasonal honey bee patterns. We applied a clustering technique in two real datasets from HiveTool.net pursuing brood temperature, relative humidity, and beehives weight. From a clustering validation index and the k-means algorithm, we have found 6 coherent patterns related to seasons. From the found patterns, we compared three well-known classification algorithms (Naive Bayes, k-NN, and Random Forest) to propose a high accuracy classification model (hit rates up to 99.67%) that suggests seasonal honey bee patterns for remote monitoring computing systems.