This project uses hive sound recordings and machine learning to detect early signs of bee swarming. By identifying acoustic differences between swarming and stable colonies, the system predicts swarming with 93% accuracy. This enables beekeepers to intervene early, prevent colony loss, and even create new healthy colonies.

This research examines whether addictive plant alkaloids like caffeine, nicotine, and morphine alter pollinator behavior. Using robotic flowers, it shows bees prefer drug-spiked nectar, learn cues faster, and may make suboptimal feeding choices. The work explores whether pollinators can develop dependency or withdrawal, suggesting plants may chemically manipulate their pollinators.