Using image-based AI for insect monitoring and conservation - InsectAI COST Action [Grant Proposal]

August, Tom ORCID: https://orcid.org/0000-0003-1116-3385; Balzan, Mario V. ORCID: https://orcid.org/0000-0002-2016-3937; Bodesheim, Paul ORCID: https://orcid.org/0000-0002-3564-6528; Brehm, Gunnar; Cantú-Salazar, Lisette ORCID: https://orcid.org/0000-0002-2818-9322; Castro, Sílvia ORCID: https://orcid.org/0000-0002-7358-6685; Chipperfield, Joseph; Ghisbain, Guillaume ORCID: https://orcid.org/0000-0003-2032-8081; Gomez-Segura, Alba ORCID: https://orcid.org/0000-0002-9575-7262; Goulnik, Jérémie; Groom, Quentin ORCID: https://orcid.org/0000-0002-0596-5376; Hogeweg, Laurens; Huijbers, Chantal ORCID: https://orcid.org/0000-0001-5206-3415; Kamilaris, Andreas ORCID: https://orcid.org/0000-0002-8484-4256; Kazlauskis, Karolis; Koch, Wouter ORCID: https://orcid.org/0000-0001-9025-9486; Korsch, Dimitri ORCID: https://orcid.org/0000-0001-7187-1151; Loureiro, João ORCID: https://orcid.org/0000-0002-9068-3954; Martin, Youri ORCID: https://orcid.org/0000-0001-8156-1587; Martinou, Angeliki F. ORCID: https://orcid.org/0000-0003-2892-8583; McFarland, Kent; Mestdagh, Xavier ORCID: https://orcid.org/0000-0001-7270-8997; Michez, Denis ORCID: https://orcid.org/0000-0001-8880-1838; Outhwaite, Charlie ORCID: https://orcid.org/0000-0001-9997-6780; Pegoraro, Luca; Pernat, Nadja; Pettersson, Lars B. ORCID: https://orcid.org/0000-0001-5745-508X; Pipek, Pavel; Preda, Cristina ORCID: https://orcid.org/0000-0002-3513-7344; Rolnick, David; Roth, Tobias; Roy, David B. ORCID: https://orcid.org/0000-0002-5147-0331; Roy, Helen ORCID: https://orcid.org/0000-0001-6050-679X; Runnel, Veljo ORCID: https://orcid.org/0000-0001-5198-8678; Sasic, Martina; Schigel, Dmitry ORCID: https://orcid.org/0000-0002-2919-1168; Sheard, Julie Koch; Svenningsen, Cecilie; Teixeira, Heliana ORCID: https://orcid.org/0000-0001-8525-9967; Titeux, Nicolas ORCID: https://orcid.org/0000-0002-7719-7417; Tscheulin, Thomas; Tzirkalli, Elli; van der Velde, Marijn; van Klink, Roel; Vereecken, Nicolas J. ORCID: https://orcid.org/0000-0002-8858-4623; Vray, Sarah ORCID: https://orcid.org/0000-0002-7385-8302; Høye, Toke Thomas. 2025 Using image-based AI for insect monitoring and conservation - InsectAI COST Action [Grant Proposal]. Research Ideas and Outcomes, 11, e134825. 40, pp. 10.3897/rio.10.e134825

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Abstract/Summary

•The InsectAI COST action will support insect monitoring and conservation at the national and continental scale in order to understand and counteract widespread insect declines. The Action will bring together a critical mass of researchers and stakeholders in image-based insect AI technologies to direct and drive the research agenda, build research capacity across Europe and support innovation and application. •There is mounting evidence that populations of insects around the world are in sharp decline. Understanding trends in species and their drivers is key to knowing the size of the challenge, its causes and how to address it. To identify solutions that lead to sustainable biodiversity alongside economic prosperity, insect monitoring should be efficient and provide standardised and frequently updated status indicators to guide conservation actions. •The EU Biodiversity Strategy 2030 identifies the critical challenge of delivering standardised information about the state of nature and image-based insect AI can contribute to this. Specifically, the EU Nature Restoration Law will likely set binding targets for the high resolution data that cameras can provide. Thus, outputs of the Action will contribute directly to EU policies implementation, where biodiversity monitoring is considered a key component. •The InsectAI COST Action will organise workshops, conferences, short-term scientific missions, hackathons, design-sprints and much more, across four Working Groups. These groups will address how image-based insect AI technologies can best address Societal Needs, support innovation in Image Collection hardware, create standardised approaches for Image Processing and develop novel Data Analysis and Integration methods for turning data into actionable insights.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.3897/rio.10.e134825
UKCEH and CEH Sections/Science Areas:	Biodiversity and Land Use (2025-) National Capability and Digital Research (2025-)
ISSN:	2367-7163
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	camera, computer vision, statistics, autonomous, standards, insects, monitoring, collaboration, inclusive, COST
NORA Subject Terms:	Ecology and Environment Computer Science Data and Information
Date made live:	19 Feb 2025 12:03 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538930

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