VespAI: a deep learning-based system for the detection of invasive hornets

• Published in: Communications biology 2024-04, Vol.7 (1), p.354-11, Article 354
• Main Authors: O’Shea-Wheller, Thomas A., Corbett, Andrew, Osborne, Juliet L., Recker, Mario, Kennedy, Peter J.
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/158/2178; Animals; Asia, Eastern; Automation; Biomedical and Life Sciences; Deep Learning; Europe; Introduced Species; Invasive species; Life Sciences; Pollinators; Vespa velutina nigrithorax; Vespidae; Wasps
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-024-05979-z

The invasive hornet Vespa velutina nigrithorax is a rapidly proliferating threat to pollinators in Europe and East Asia. To effectively limit its spread, colonies must be detected and destroyed early in the invasion curve, however the current reliance upon visual alerts by the public yields low accuracy. Advances in deep learning offer a potential solution to this, but the application of such technology remains challenging. Here we present VespAI, an automated system for the rapid detection of V. velutina . We leverage a hardware-assisted AI approach, combining a standardised monitoring station with deep YOLOv5s architecture and a ResNet backbone, trained on a bespoke end-to-end pipeline. This enables the system to detect hornets in real-time—achieving a mean precision-recall score of ≥0.99—and send associated image alerts via a compact remote processor. We demonstrate the successful operation of a prototype system in the field, and confirm its suitability for large-scale deployment in future use cases. As such, VespAI has the potential to transform the way that invasive hornets are managed, providing a robust early warning system to prevent ingressions into new regions. A deep learning-based system enables the rapid detection and classification of the invasive hornet Vespa velutina , providing an automated surveillance capability at the invasion front.