The genus Agrilus includes two species, Agrilus planipennis and A. anxius, that are of particular phytosanitary concern and that are regulated by the European Union legislation. This implies that phytosanitary agencies of all EU countries are obliged to establish specific surveillance programs to verify the absence of these species from their territory. These activities commonly consist of the use of green-colored traps, which are however attractive not only for A. planipennis and A. anxius, but also for a wide range of other Agrilus species. For this reason, much time and expertise is required to sort and identify specimens to species, impeding an efficient rapid response. In this study, we tested the efficacy of the Entomoscope, a low-cost, open-source photomicroscope that uses high-resolution digital imaging and allows a pre-trained Convolutional Neural Networks (CNN) model to accurately detect, image and classify insect specimens, for automatic identification of 13 Agrilus species, including A. planipennis and A. anxius. We benchmarked models from three different CNN architectures and selected YOLOv8l as the most robust performer; this model achieved a Top-1 accuracy of 90.2% on a “real-world” test set (i.e. a dataset simulating real surveillance conditions). For most species, including A. planipennis and A. anxius, either no errors or only a few errors were made, whereas for a few native species misidentifications were more common. These results provided proof of concept for an AI-driven surveillance system that can strongly aid in surveillance activities of Agrilus species.

Agrilus planipennis, Agrilus anxius, Bronze birch borer, Deep learning, Early-detection, Emerald ash borer, Entomoscope