Exeter researchers unveil an AI powered hornet detector and identifier
Researchers from a British university have introduced VespAI, a tool that relies on artificial intelligence to attract Asian hornets to a monitoring station and identify them through camera footage. The system represents a fusion of compact hardware and advanced neural networks designed to work together in real time. The team emphasizes that VespAI is built to enhance field surveillance and rapid identification, enabling authorities and researchers to react quickly to hornet incursions while collecting valuable data on when and where these insects appear.
The Asian hornet, also known as the yellow-legged or yellow-footed hornet, is native to parts of South and Southeast Asia. In recent years, its range has expanded into North America and large portions of Europe. This species is particularly concerning because hornets can be dangerous to humans when they feel threatened. A hornet sting delivers venom that can trigger severe reactions, including anaphylaxis in susceptible individuals. In dense swarms, stings pose a real risk and can lead to serious health emergencies, underscoring the need for effective monitoring and timely responses to incursions.
VespAI operates with a compact processor that remains in sleep mode until a sensor detects an insect of wasp size. When the system identifies movement consistent with a hornet, the AI algorithm activates and processes the captured image to determine whether the insect is Vespa velutina, the Asian hornet, or Vespa crabro, the native European hornet. If the algorithm confirms an Asian hornet, the sensor promptly sends an alert containing the image to designated users, allowing human reviewers to verify the identification and decide on appropriate action. This workflow minimizes false alarms and ensures that alerts are both timely and accurate while preserving resources on the ground for verification and follow up actions.
Experts say that VespAI is designed to improve efforts to manage Asian hornet populations by providing precise, rapid detection without unduly harming beneficial insects or triggering ineffective trapping. Traditional traps can inadvertently capture non target species and may not significantly reduce the number of harmful hornets in an area. The AI guided system offers a more targeted approach by confirming the threat before triggering defensive measures, potentially reducing collateral impacts on ecosystems while focusing attention where it matters most.
Researchers also note that ongoing genetic studies continue to shed light on how hornets adapt to new environments and spread across continents. That scientific work helps explain why some populations establish themselves so readily in unfamiliar territories. VespAI complements these insights by delivering real time data and rapid verification capabilities that support proactive management strategies, enabling communities to respond faster and with greater confidence when hornet activity is detected.