Researchers use machine learning to find bee-safe pesticide repellents
A team at the University of California, Riverside published a study in eLife describing how they used machine learning to identify chemicals that repel honey bees from crops.
New method targets bee safety
The interdisciplinary group led by Professor Anandasankar Ray developed a model to screen millions of potential compounds. Their goal was to find scents that push bees away without harming them. This approach addresses the decline in pollinator populations caused by pesticide exposure.
Complexity of bee senses
Honey bees possess more than 200 odor receptors that detect many volatile compounds. Finding a scent to deter them was difficult because their sense of smell is highly sensitive. The new system overcomes this obstacle by identifying specific deterrents effectively.
Collaboration with entomology experts
The researchers worked with Boris Baer and his lab to create the machine-learning model. This partnership combined molecular biology with insect olfactory behavior expertise. The paper detailing these findings is now available in the journal eLife.
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