Bee intelligent: AI-driven approaches to hive and colony management

Abstract

Honey bees (Apis mellifera) are vital for farming and nature, but they face major threats from parasites, diseases, pesticides, and climate change, leading to significant colony losses. Manual hive checks are time-consuming, disruptive, and provide little early warning, highlighting the need for better methods. Artificial Intelligence (AI) offers solu-tions through sensors, computer vision, data analysis, and robotic hives. These enable continuous monitoring, early risk alerts, and smarter management. Current applications include sensors to track stress and swarming, computer vision to count bees and detect diseases, analytics to forecast honey yield and pesticide risks, and robotic hives for long-term behavior tracking. Future work may integrate multiple data sources, apply swarm-inspired models, study bee genetics, and connect with ecological planning. Although challenges such as cost and data quality remain, AI is expected to become a central tool for resilient beekeeping, sup-porting colony health, food security, and ecosystems. For Türkiye, one of the world’s top honey producers, AI also presents a strategic opportunity to strengthen national apicul-ture and enhance global competitiveness.