Determination of flowering stage based on artificial intelligence and the daily weight of bee hives

Honey bee plays a very important role in pollination and is essential for the balance of terrestrial ecosystems and in the pollination of important crops. The success of honey bee hives and beekeeping depends on the flowering period, and good hive management during this period is essential for beekeepers. The use of new technologies in beekeeping can help this farming activity enormously. Based on a monitoring system of several hives located in the south of Spain, this work presents a study of the data obtained to find out if there is a relationship between these data and the flowering stage of the hives. In this study, it is determined that the evolution of the weight of the hive throughout the day is crucial to determine the flowering stage. By testing the behavior of several machine learning algorithms, a highly efficient classifier is obtained, capable of determining which stage of flowering the hives are in. It is able not only to determine whether the hives are before, during or after flowering, but also to distinguish between an initial and final stage of flowering. This is important because it can enable beekeepers to effectively plan apiary visits, hive maintenance work and honey harvesting, making beekeeping more profitable. • Monitoring system based on weight sensors for continuous data acquisition in bee hives. • Discretization of flowering stages into four classes according to bee activity. • Artificial intelligence methods applied to the classification of flowering stages to enhance the knowledge of beekeepers.