AI-Driven Autonomous Bee Health and Ecosystem Management System

Abstract

Global honeybee population decline continues to threaten agricultural productivity and ecological stability, with annual colony losses exceeding 35%. Traditional hive inspections are labor-intensive, disruptive, and inadequate for early detection of diseases and environmental stress. This study presents an AI-driven autonomous bee health and ecosystem management system that combines IoT-based sensing, machine learning, and edge computing to enable real-time hive monitoring and intelligent automation. The system integrates four functional modules: environmental monitoring with time-series forecasting, threat detection and autonomous control, AI-optimized hive site selection using geospatial analytics, and multimodal health assessment via visual and acoustic data. Field evaluations conducted across multiple apiaries in Sri Lanka achieved 92.6% accuracy in bee health assessment and 88.7% recall in threat detection, while improving honey yield by 23% compared with traditional methods. The proposed solution demonstrates how multimodal AI and IoT integration can advance sustainable apiculture through proactive, data-driven decision-making.