Project: Easy RIDER (Real-time IDentification for ecological research and monitoring)
Funder: UKRI Natural Environment Research Council Global Partnerships Seedcorn Fund
Principal investigator: Dr David Roy, UK Centre for Ecology & Hydrology
Insects are recognised as a dominant component of almost all ecosystems, but there are growing concerns that insect biodiversity has declined globally, with serious consequences for the ecosystem services on which we all depend. Critically, we lack primary data from the most biodiverse parts of the world but progress in insect monitoring to date has been hampered by many technical challenges.
Automated sensors, deep learning and computer vision offer the best practical and cost-effective solution for more standardised monitoring of insects across the globe. Inter-disciplinary research teams are needed to meet this challenge.
Easy RIDER aims to develop new international partnerships and networks to underpin the development of long-term and sustainable collaborations for this exciting, yet nascent, research field that spans engineering, computing and biology.
The project has four main activities:
- Interactive, online and face-to-face engagement between academic and practitioner stakeholders, including key policy-makers, via online webinars and at focused knowledge exchange and grant-writing workshops in Canada and Europe;
- Knowledge exchange between the UK, Europe and North America, to share practical experience of building and deploying sensors, develop deep learning and computer vision for insects, and to build data analysis pipelines to support research applications;
- A proof-of-concept field trial spanning the UK, Denmark, the Netherlands, Canada, USA and Panama. Testing automated sensors against traditional approaches in a range of situation;
- Dissemination of shared learning throughout this project and wider initiatives, building a new community of practice through a WildLabs group to develop with a shared vision for automated insect monitoring technology to meet its worldwide transformational potential.