UK Varsity Develops AI to Detect Asian Hornets
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UK Varsity Develops AI to Detect Asian Hornets
By Alabidun Shuaib AbdulRahman
Scientists at the University of Exeter have developed an artificial intelligence (AI) system, dubbed VespAI, aimed at detecting invasive Asian hornets.
The automated system, designed by researchers at the university, functions by attracting hornets to a monitoring station and capturing standardized images.
According to the university, VespAI exhibits “almost perfect accuracy” in identifying the species.
Dr. Thomas O’Shea-Wheller from the Environment and Sustainability Institute highlighted the system’s goal of being “cost-effective and versatile,” accessible to various stakeholders, including governments and beekeepers.
He emphasized the promising results of the prototype version, stating that VespAI could serve as a robust early warning system for detecting Asian hornet incursions into new regions.
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The University of Exeter elaborated that VespAI employs a compact processor and remains dormant until its sensors detect an insect within the size range of a hornet.
Upon detection, INCNews247 learnt the system’s AI algorithm activates to analyze the image and determine whether it depicts an Asian hornet (Vespa velutina) or a native European hornet (Vespa crabro). If an Asian hornet is identified, the monitor sends an image alert to the user for confirmation.
In 2023, the UK witnessed “record numbers” of Asian hornet sightings, posing a significant threat to honeybee populations and biodiversity.
The invasive hornets have been observed in various regions, including East Sussex, Kent, Devon, and Dorset.
Dr. Peter Kennedy, the mastermind behind the system, emphasized the importance of accurate identification, as many reports are often misidentified.
He highlighted VespAI’s role in providing vigilant, accurate, and automated surveillance without harming non-target insects.
During the testing phase, the system demonstrated its effectiveness in Jersey, where high numbers of Asian hornet incursions occur due to its proximity to France.
Dr. O’Shea-Wheller underscored the system’s high accuracy, ensuring the correct identification of Asian hornets without falsely identifying other species.
The research project involved collaboration between biologists and data scientists from the University of Exeter’s Environment and Sustainability Institute, Centre for Ecology and Conservation, and Institute for Data Science and Artificial Intelligence.
