20.500.12556/DiRROS-27927 Image-based recognition using advanced neural networks can aid surveillance of Agrilus jewel beetles 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 programmes to verify the absence of these species from their territory. These activities commonly consist of the use of green-coloured 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 anxius Agrilus planipennis bronze birch borer deep learning early-detection emerald ash b4orer Entomoscope Agrilus anxius Agrilus planipennis bronasti brezov venec globoko učenje zgodnje odkrivanje smaragdni jesenov venec entomoskop true false true Angleški jezik Slovenski jezik Znanstveno delo 2026-02-27 12:55:41 2026-02-27 12:55:42 2026-02-28 03:48:21 0000-00-00 00:00:00 2026 0 0 Nasl. z nasl. zaslona; Opis vira z dne 27. 2. 2026; Skupno št. avtorjev: 16; Avtorja iz Slovenije: M. de Groot, Eva Groznik; str. 319-336 Vol. 105 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 630* 1314-2488 10.3897/neobiota.105.180959 269935363 https://neobiota.pensoft.net/article/180959/element/8/57811// 0 541f77c0-13d3-11f1-8c73-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=41126 Gozdarski inštitut Slovenije 0 0 0