Digital twin approaches for honey bee colony health: opportunities, challenges, and future directions

Abstract

Honey bee colonies face increasing threats from parasites, pathogens, pesticides, and cli-mate change, resulting in substantial global losses. Digital Twin (DT) technology, which is widely adopted in manufacturing and increasingly explored in agriculture, offers a dy-namic way to link real-time data with simulation models for prediction and decision support. In this review, we examine the potential of DT approaches tailored to apiculture. While existing tools such as BEEHAVE, Beescape, and HONEYBEE-pDT provide valuable in-sights into specific aspects of colony dynamics or environmental risks, they remain frag-mented, lack real-time data integration, and are often calibrated for non-local conditions, limiting their practical use in regions such as Türkiye. Adapting these frameworks with locally sourced data, IoT technologies, and user-friendly interfaces presents a unique op-portunity to build predictive and regionally relevant decision-support systems. A DT sys-tem for honey bees would integrate biological modules (Varroa mite, Nosema, and virus-es), environmental modules (climate, forage, and pesticides), and performance outputs (honey yield and colony survival). By enabling scenario testing such as untreated Varroa infestations, organic acid treatment schedules, or heat stress conditions, DT frameworks could deliver early warnings and guide sustainable colony management. Türkiye’s position as one of the world’s leading honey producers underscores both the urgency and the potential to pioneer digital twin applications in apiculture. Beyond na-tional relevance, the development of such systems could reduce colony mortality, opti-mize management, and enhance resilience against climate change, while also serving as a transferable model for digital agriculture worldwide.