Potato (Solanum tuberosum L.) is a global crop with a vital role in food security and economic significance in many countries. Potato virus Y (PVY) is one of the major viral threats, causing severe yield and quality losses when not controlled properly. PVY spreads primarily through aphids and infected tubers, and current management relies on insecticides and protective oils. Novel detection methods are needed to identify infected plants accurately at an early stage of plant development, thus reducing pesticide use. Trained honeybees (Apis mellifera L.) can detect specific volatiles emitted by plants infected by viruses like PVY. Using associative conditioning and the proboscis extension reflex, we tested the capacity of harnessed worker bees to distinguish PVY-infected and healthy potato leaves as a first step towards field application. As a whole, the results were impeded by low response levels and no significant result was obtained. However, we were able to show the capability of honeybees to learn and differentiate between two conditioned stimuli (healthy potato leaves vs clean air). Our findings therefore suggest that honeybees, as a globally accessible resource, have the potential to be used as a cost-effective solution in crop health monitoring, with further investigation and protocol refinement to achieve accurate PVY detection in agricultural settings.