An Integrated IoT–AI Architecture for Precision Beekeeping: Sensing, Data Communication, Colony-State Intelligence, and Decision-Oriented Actions

Abstract

Precision beekeeping increasingly adopts Internet of Things (IoT) and artificial intelligence (AI) technologies, yet most existing systems remain monitoring-centric. This study synthesizes the architectural characteristics of IoT–AI precision beekeeping systems and identifies integration gaps that constrain decision-oriented operation. A systematic literature review of 50 Scopus-indexed studies published between 2015 and 2024 was conducted using a PRISMA-based selection process and an architecture-oriented synthesis across sensing, communication, intelligence, and decision layers. The results reveal a strong emphasis on sensing and data acquisition, while analytical outputs are weakly linked to operational decision-making, preventing most systems from closing the loop from inference to action. These findings suggest that the main limitation is architectural rather than technological. Accordingly, this study positions a reference architecture as an analytical framework for end-to-end smart beehive systems, with implications for more integrated and practical applications in small- and medium-scale beekeeping operations.