Prediction of the remaining time of the foraging activity of honey bees using spatio-temporal correction and periodic model re-fitting

Abstract

The problem of bee poisoning causes significant losses to the beekeeping sector every year. One cause of bee poisoning is spraying before the end of the foraging activity of bees. Information about the estimated end of this foraging activity can significantly help a farmer plan his spraying. The aim of our research was to develop a method based on machine learning models to predict the remaining time of the foraging activity, taking into account bee activity, weather conditions, and the amount of time to sunset. Data were collected using an IoT system from 3 hives in the 2021 and 2022 beekeeping seasons. The proposed method addresses the challenge of the changing nature of data during the beekeeping season by using periodic model re-fitting with automatically generated semi-true target values. The veracity of semi-true target values was also improved by a spatio-temporal correction mechanism based on the position and orientation of the bees, which made it possible to distinguish foraging from other patterns of bee behavior (dead bees, hive ventilation by bees). The results of the RMSE prediction error of 23.1 min (season 2021) and 26.5 min (season 2022) prove the high potential of the proposed method to predict the remaining time of the foraging activity of bees, as well the lack of need for expert annotation of data during the season. The used approach, based on density occurrence maps in spatio-temporal correction, can also be used in the future to detect and study bee behavior patterns.