Image-based recognition using advanced neural networks can aid surveillance of Agrilus (Coleoptera, Buprestidae) jewel beetles

Preprint ARPHA Preprints https://doi.org/10.3897/arphapreprints.e154842 (04 Apr 2025) https://doi.org/10.3897/arphapreprints.e154842 (04 Apr 2025) Other versions: - Preprint InfoPreprint Info - CiteCite - MetricsMetrics - CommentComment - RelatedRelated - CitedCited ARPHA Preprints doi: 10.3897/arphapreprints.e154842 First posted 04 Apr 2025 Authors Valerio Caruso - Corresponding author Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020, Legnaro, Italy Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), EFNO, Nogent-Sur-Vernisson, France Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University of Roma, Roma, Italy Museo Civico di Storia Naturale, Parco Cascina Vigna, Carmagnola, Italy Slovenian Forestry Institute, Department of Forest Protection, Večna Pot 2, 1000, Ljubljana, Slovenia University of Ljubljana, Biotechnical Faculty, Jamnikarjeva ulica 101, 1000, Ljubljana, Slovenia Slovenian Forestry Institute, Department of Forest Protection, Večna Pot 2, 1000, Ljubljana, Slovenia Department of Natural Forests, Forest Research Institute, 6 Park Dyrekcyjny St., 17-230, Białowieża, Poland Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), UR 0633, Zoologie Forestière, 45075, Orléans, France Laboratoire Physiologie, Ecologie et Environnement (P2E), Université d’Orléans, UR 1207, USC-INRAE 1328, 1 rue de Chartres, Orléans 45067, France Natural Resources Canada, Canadian Forest Service - Atlantic Forestry Centre, 1350 Regent St., Fredericton, NB, E3C 2G6, Canada Institute for Automation and Applied Informatics (IAI), Karlsruhe Institute of Technology (KIT), 76344, Eggenstein-Leopoldshafen, Germany Davide Rassati - Corresponding author Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020, Legnaro, Italy Conflict of interest The authors have declared that no competing interests exist. Supporting agencies This work was supported by the project “Improving Forest Insect Biosecurity in an era of globalization – FORBIO”, funded by the European Union-Next Generation EU, Mission 4, Component 1, CUP:C53D23009620001 and partially by the European Union’s Horizon Europe Research and Innovation programme under grant agreement No. 101134200 "FORSAID: Forest surveillance with artificial intelligence and digital technologies". MdG and EG were funded by the Administration of the Republic of Slovenia for Food Safety, Veterinary Sector and Plant Protection (Contract No. C2337-23-000026) and Slovenian Research Agency (Research Programme P4-0107) This is an open access preprint distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.