Scientists at the University of Exeter have developed an artificial intelligence (AI) system called VespAI to detect invasive Asian hornets. This automated system attracts hornets to a monitoring station and captures standardized images, achieving almost perfect accuracy in identifying the species, according to the university.
Dr. Thomas O’Shea-Wheller from the Environment and Sustainability Institute at Exeter’s Penryn Campus emphasized the goal of creating a cost-effective and versatile solution that can be used by various stakeholders, from governments to individual beekeepers. The initial testing of VespAI in the Channel Islands yielded encouraging results, demonstrating its potential as a robust early warning system for detecting Asian hornets entering new regions.
The system operates using a compact processor that remains dormant until its sensors detect an insect within the size range of a hornet. Once activated, VespAI’s AI algorithm analyzes the captured image to determine if it is an Asian hornet or a native European hornet. If an Asian hornet is identified, an image alert is sent to the user, enabling confirmation of the identification.
The University of Exeter highlighted the urgent need for such technology, citing record numbers of Asian hornet sightings in the UK in 2023. These invasive hornets pose a significant threat to honeybees, with just one hornet capable of killing up to 50 bees per day. The UK response strategy relies on people spotting, identifying, and reporting hornets.
Dr. Peter Kennedy, the conceptualizer of the system, emphasized the importance of accurate surveillance to address misidentifications commonly encountered in reports. VespAI aims to provide vigilant and automated surveillance without harming non-target insects, ensuring that live hornets can be tracked back to their nests for effective eradication.
The system was tested in Jersey, which experiences high numbers of Asian hornet incursions due to its proximity to France. Dr. O’Shea-Wheller highlighted VespAI’s high accuracy, which minimizes misidentifications and ensures that no Asian hornets go undetected. The research project involved collaboration between biologists and data scientists from various institutes at the University of Exeter.
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