*NOTE-this is not a fisheries specific webinar but using AI and remote sensing is a rapidly evolving technique and can be applied across a variety of sciences. This information could help aquatic specialists explore new ways to detect aquatic invaders. The webinars will highlight how emerging technologies are being adapted and applied to meet the challenges of invasive species detection in the field—providing a glimpse into the future of smart conservation.

Join NAISMA this November for a dynamic webinar showcasing how artificial intelligence is transforming invasive species detection and monitoring across ecosystems. Through innovative applications like smart traps, drones, and machine learning, researchers and practitioners are unlocking new tools to manage biological invasions more effectively and efficiently.

Dr. Melissa Miller from the University of Florida will present her work on developing AI-powered smart traps designed to detect and remove invasive tegu lizards—large, fast-moving reptiles that threaten native wildlife and agriculture in the southeastern U.S. Dr. Thomas O’Shea-Wheller from the University of Exeter will share his team’s research on using deep learning models to detect invasive hornets in real time, offering critical insights for rapid response and containment. Representing Ducks Unlimited Canada, Matthew Bolding and Mallory Carpenter will discuss their efforts to integrate drone technology and AI to monitor populations of European water chestnut, a fast-spreading aquatic invasive plant impacting wetland biodiversity and water quality.

VespAI: Applying Deep Learning to the Detection of Invasive Hornets presented by Thomas O’Shea-Wheller
The invasive hornet Vespa velutina nigrithorax is a rapidly proliferating threat to biodiversity and apiculture in Europe, East Asia, and North America. To date, authorities have struggled to contain the hornets, as colonies must be detected and destroyed early in the invasion curve if establishment is to be prevented. Current monitoring approaches rely primarily upon visual alerts by the public and surveillance trapping, however the former yields less than 0.01% accuracy, while the latter kills substantial numbers of native invertebrates. With the continuing spread of V. velutina, there is thus a pressing need to develop improved monitoring technologies within a limited timeframe. In this talk, I outline VespAI, an automated system for the rapid detection and behavioural quantification of V. velutina, V. crabro, and V. orientalis. VespAI leverages a hardware-assisted AI approach, combining a standardised monitoring station with deep YOLO architecture, trained on a bespoke end-to-end pipeline. This enables the system to detect hornets in real-time—achieving a precision-recall score of ≥0.99—and send associated image alerts via a compact remote processor. I discuss the development, performance, and future deployment of the system, and highlight its potential to enhance the scope and sustainability of invasive hornet surveillance at a global scale.

This webinar will highlight how emerging technologies are being adapted and applied to meet the challenges of invasive species detection in the field—providing a glimpse into the future of smart conservation.