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

Abstract

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.

Deep learning-based detector of invasive alien frogs,Polypedates leucomystax and Rhinella marina, on an island at invasion front

“…Recently, several studies attempted to detect invasive alien species in the wild with deep learning. Examples include detecting Anolis lizards (Aota et al 2021), lionfish (Martínez-González et al 2021), and Asian black hornets (O'Shea-Wheller et al 2024) from images, and barred owls (Kelly et al 2023) and bullfrogs (Bota et al 2024) from sounds. These studies suggest that a deep learning model can detect invasive alien species when local training samples (i.e., samples collected at sites of actual application) are available.…”