What if AI could help bees defend themselves from the Asian hornet? The revolutionary trap designed by French students

When faced with a hive under attack, the problem is immediately apparent. The bees slow down, stay closer to the entrance, fly less. The Asian hornet patrols the hive like a waiting predator, takes position and consumes the colony’s energy just by its presence. The nest, however, is elsewhere. Often high up, hidden among the branches. Or in a hedge, in a green area, in a difficult to reach place. For beekeepers the trouble begins right there: seeing the threat is easy, finding its origin takes time.

The scientific name is Vespa velutina nigrithorax, also known as the Asian yellow-legged hornet. It has a dark livery, legs with yellow tips, slightly smaller dimensions than the European hornet and strong predatory pressure on bees. It arrived in Italy in 2012, with the first reports in Liguria, then it moved to other areas of the peninsula, from Tuscany to Piedmont, with presences also recorded in Lombardy, Veneto, Emilia-Romagna and Sardinia. The map remains to be continuously updated, because the species advances through outbreaks and nests identified in the area.

This includes Safe To Bee, a connected trap developed in France and designed to do two very concrete things: selectively capture the Vespa velutina and help locate the nests. The device uses a bait, a targeted trapping system and an artificial intelligence camera to follow the trajectory of live hornets when they are released. The goal is to figure out where they return, then narrow down the area to search for the nest.

The trap that observes

The idea is simple to explain, less simple to make work in the field. The trap intercepts the Asian hornet near the hives, signals when it fills and releases some live specimens. At that point the camera follows them as they leave again. Multiple observations, from different points, can help estimate the direction of the nest through a triangulation system. In practice: instead of searching randomly among trees, roofs and hedges, we try to follow the insect’s return route.

The project also declares another important feature: selectivity. Traps against Vespa velutina are a delicate topic, because an imprecise device risks also catching bees, butterflies and other useful insects. Here the attempt is to narrow the target, reducing the impact on other pollinators. Safe To Bee is presented as an autonomous, connected system, also powered by small solar panels and connected to an application to monitor the device and facilitate contact with nest removal professionals.

The project is promising, but it still needs to be measured outside the technical data sheet. We need to understand how well the system holds up in real conditions: rain, wind, distance, vegetation, position of the apiaries, density of the nests, behavior of the insects. A trap really works when it stops being a good idea and starts working next to beehives.

The risk, when talking about invasive species, is moving from one simplification to another. On the one hand the generic alarm, on the other the saving solution. The Asian hornet requires another posture: monitoring, correct reporting, targeted interventions, selective devices, coordination between beekeepers and local institutions. Safe To Bee enters this framework, with a useful idea precisely because it tries to reduce the waste of time and the impact on the wrong insects.

A trap with artificial intelligence alone will hardly change the fate of bees. However, he can become a useful piece within a broader strategy, if he manages to demonstrate precision, resistance and true selectivity on the field. Bees already work hard enough. At least someone has to go and look for the nest better.

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