University of California machine learning spots scents bees avoid in real fields

University of California researchers have applied machine learning to identify scents that repel honey bees, a discovery with potential to protect these vital pollinators from pesticide exposure. The team’s approach goes beyond traditional methods, predicting repellent compounds and then validating those predictions both in laboratory settings and within real agricultural fields. “This important manuscript reports a very interesting view of how pesticides can be toxic to beneficial insects like the honeybee,” says Barbara F Baer-Imhoof of the University of California, Riverside. This research impacts ecology, pest control, and sensory biology by offering a new way to rationally discover aversive volatiles for insect control. An expanded computational model screened over 50 million compounds, ultimately identifying more than 130 candidate repellent substances for honey bees. Behavioral tests demonstrated a high degree of predictive accuracy for these compounds, a result subsequently validated through field studies assessing repellency against foraging bees near pesticide-treated crops. This dual validation, in controlled conditions and real-world environments, underscores the potential for machine learning to accelerate the discovery of effective insect repellents. The challenge of identifying these repellents experimentally is considerable; despite two decades of research into insect olfactory receptors, the…