Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

Roy, D.B. ORCID:; Alison, J.; August, T.A. ORCID:; Bélisle, M.; Bjerge, K.; Bowden, J.J.; Bunsen, M.J.; Cunha, F.; Geissmann, Q.; Goldmann, K.; Gomez-Segura, A.; Jain, A.; Huijbers, C.; Larrivée, M.; Lawson, J.L.; Mann, H.M.; Mazerolle, M.J.; McFarland, K.P.; Pasi, L.; Peters, S.; Pinoy, N.; Rolnick, D.; Skinner, G.L.; Strickson, O.T.; Svenning, A.; Teagle, S.; Høye, T.T.. 2024 Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects [in special issue: Towards a toolkit for global insect biodiversity monitoring] Philosophical Transactions of the Royal Society B: Biological Sciences, 379 (1904).

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Automated sensors have potential to standardize and expand the monitoring of insects across the globe. As one of the most scalable and fastest developing sensor technologies, we describe a framework for automated, image-based monitoring of nocturnal insects—from sensor development and field deployment to workflows for data processing and publishing. Sensors comprise a light to attract insects, a camera for collecting images and a computer for scheduling, data storage and processing. Metadata is important to describe sampling schedules that balance the capture of relevant ecological information against power and data storage limitations. Large data volumes of images from automated systems necessitate scalable and effective data processing. We describe computer vision approaches for the detection, tracking and classification of insects, including models built from existing aggregations of labelled insect images. Data from automated camera systems necessitate approaches that account for inherent biases. We advocate models that explicitly correct for bias in species occurrence or abundance estimates resulting from the imperfect detection of species or individuals present during sampling occasions. We propose ten priorities towards a step-change in automated monitoring of nocturnal insects, a vital task in the face of rapid biodiversity loss from global threats.

Item Type: Publication - Article
Digital Object Identifier (DOI):
UKCEH and CEH Sections/Science Areas: Biodiversity (Science Area 2017-)
Hydro-climate Risks (Science Area 2017-)
ISSN: 0962-8436
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link.
Additional Keywords: biodiversity monitoring, machine learning, moths, camera trap
NORA Subject Terms: Ecology and Environment
Data and Information
Date made live: 13 May 2024 12:28 +0 (UTC)

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