ArticlePhilosophical transactions of the Royal Society of London. Series B, Biological sciences2024
Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.
Article in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds.Journal of insect science (Online) · 2026Article
- Terrestrial Arthropod Responses to Arctic Cryosphere Degradation.Global change biology · 2026Review
- Computer Vision for Monitoring Wild Bees and Wasps: A Structured Literature Review.Ecology and evolution · 2026Review
- Globally aggregated biodiversity data impact predictive and descriptive research.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline.PNAS nexus · 2025Article
- Omics in mini-livestock: a genomic perspective on the future of sustainable food systems.Frontiers in genetics · 2025Review
- Towards a toolkit for global insect biodiversity monitoring.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Emerging technologies in citizen science and potential for insect monitoring.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Delivering on a promise: futureproofing automated insect monitoring methods.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
27 authors.
Funding
Abstract
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. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.