AI-Driven Hive Management: Optimizing and Enhancing Honey Production through AI and Sensor-Based System

In traditional beekeeping, hive management heavily depends on manual inspections, which are time-consuming, labor-intensive, and prone to human error, often resulting in delayed interventions for issues like hive congestion, disease, or swarming. This paper proposes an AI-driven hive management system to optimize beekeeping by employing advanced technologies, including YOLOv9-based camera monitoring, load cell weight tracking, and sound sensors to provide real-time insights into hive conditions. The system identifies critical events such as hive replacement needs and swarm prediction by analyzing bee activity, hive weight, and sound patterns, thus enabling proactive beekeeping strategies. The solution integrates cloud-based data storage and analysis using AWS services, providing beekeepers with timely alerts through SMS notifications. This AI-powered approach improves hive monitoring accuracy, reduces labor, and enhances honey production by preventing hive losses and improving hive health management.