Buzzing with Intelligence: A Systematic Review of Smart Beehive Technologies
Josip Šabić1, Toni Perković1, Petar Šolić1
1Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture in Split, University of Split, 21000 Split, Croatia.
Smart beehives use IoT sensors and machine learning for data-driven beekeeping. This technology enables proactive management, improving bee health and productivity through early problem detection and condition monitoring.
Area of Science:
- Agricultural Technology
- Computer Science
- Environmental Science
Background:
- Traditional beekeeping is reactive; smart beehives offer proactive, data-driven management.
- Intelligent systems are transforming apiculture towards precision agriculture.
Purpose of the Study:
- To systematically review intelligent systems in beehives.
- To analyze IoT, sensor modalities, and machine learning in precision apiculture.
- To identify trends and challenges in smart beekeeping technology.
Main Methods:
- Systematic literature review adhering to PRISMA guidelines.
- Analysis of 135 peer-reviewed publications (1990-2025).
- Searches conducted on Web of Science, IEEE Xplore, and Scopus.
Main Results:
- Common sensors include environmental, acoustic, visual, and structural modalities.
- Deep learning, computer vision, and multimodal sensor fusion are emerging trends.
- Growing availability of public datasets for machine learning model training.
Conclusions:
- Smart beehives enhance early problem detection, hive monitoring, and predictive intervention.
- Challenges include system integration, dataset standardization, and scalability.
- Advancements aim to improve colony health, productivity, and resilience.
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