Digital Monitoring and Modeling of Honey Bee Foraging on Clover Flowers Under Heat Stress.

Abstract

Plant-pollinator networks are increasingly threatened by rising global temperatures, yet monitoring these interactions at scale remains challenging. This study presents a digital monitoring framework that leverages artificial intelligence (AI) and machine learning (ML) to analyze honey bee ( Apis mellifera ) foraging on white clover ( Trifolium repens ) under normal and heat-stress conditions. The main objective was to establish a proof-of-concept for real-time, AI-driven pollinator monitoring rather than to draw definitive conclusions about the effects of heat stress on honey bee foraging behavior. This advancement supports smart farming decisions, such as optimizing clover buffer placement and adaptive honey bee management under heat stress.