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 how honeybee queens adjust egg size in response to their environment. Queens in food-rich urban areas lay smaller eggs, while those in rural areas lay eggs 45% larger, producing bees that forage earlier and more often. These findings can guide beekeeping and support pollinator health, crucial for global food supply.