Precision Apiculture with Artificial Intelligence: A Survey of Methods, Tools, and Future Directions

Beekeeping plays a vital role in maintaining biodiversity, ecosystem balance, and global food security through pollination and the production of honey and other bee products. However, apiculture faces increasing threats from disease outbreaks, colony collapse disorder, and environmental stressors. In recent years, the convergence of artificial intelligence (AI), bio-inspired computation, and precision agriculture has introduced new tools for sustainable and intelligent beekeeping. This review aims to provide a comprehensive overview of AI applications in beekeeping, with an emphasis on both theoretical approaches and existing tools. It further explores the role of bio-inspired algorithms, such as the Artificial Bee Colony (ABC), in linking natural swarm behaviour to computational optimisation problems. The most prominent AI implementations are found in disease diagnostics, using Computer Vision (CV) and Deep Learning (DL) for the detection of pathogens such as Varroa destructor and Nosema. Behavioural tracking through video analytics and acoustic monitoring is gaining momentum, offering early warnings for colony stress and swarming. While commercial tools demonstrate practical feasibility, adoption remains uneven due to cost, infrastructure needs, and limited access among small-scale beekeepers. However, data scarcity and model generalisability remain significant limitations. The integration of AI and evolutionary computation into precision beekeeping presents a transformative opportunity to enhance sustainability, productivity, and resilience in apiculture. The interdisciplinary nature of intelligent apiculture opens up opportunities for collaboration across fields such as computer science, agriculture, ecology, and ethics. This study establishes a foundation for future research in AI-driven beekeeping, aligned with the goals of sustainable agriculture and environmental stewardship.