August, Tom;
Balzan, Mario;
Bodesheim, Paul;
Brehm, Gunnar;
Cantú-Salazar, Lisette;
Castro, Sílvia;
Chipperfield, Joseph;
... Høye, Toke Thomas; + view all
(2025)
Using Image-based AI for insect monitoring and conservation - InsectAI COST Action.
Research Ideas and Outcomes
, 11
, Article e134825. 10.3897/rio.10.e134825.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Using Image-based AI for insect monitoring and conservation - InsectAI COST Action |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3897/rio.10.e134825 |
| Publisher version: | https://doi.org/10.3897/rio.10.e134825 |
| Language: | English |
| Additional information: | This is an open access article 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. |
| Keywords: | camera, computer vision, statistics, autonomous, standards, insects, monitoring, collaboration, inclusive, COST |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Div of Biosciences > Genetics, Evolution and Environment |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204569 |
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