Integration of 5.8GHz Doppler Radar and Machine Learning for Automated Honeybee Hive Surveillance and Logging

A 5.8GHz Doppler radar was used to monitor free flying honeybees entering and leaving their hive at a 2m distance. Free falling metal spheres of different size and materials were first used, along with radar cross section (RCS) simulations, for calibration of an in house continuous-wave (CW) radar system. The system was then applied to extract the RCS of free flying honeybees (n=164) at 5.8GHz, which fills a gap in the literature and was found to be in the range of -55 to -60dBsm +/- 3dBsm. The Doppler radar was hence integrated with machine learning (ML) techniques to autonomously discriminate the incoming and outgoing flights of honeybees. A neural network built through a random forest algorithm and processing of the data as Line Spectral Pairs (LSPs) achieved a maximum accuracy of 87.83% with a Binary Cross Entropy loss of 0.4274 when interpreting hive departure/entrance events.