Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring

Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring

Abstract Pollinating insects provide essential ecosystem services, and using time-lapse photography to automate their observation could improve monitoring efficiency. Computer vision models, trained on clear citizen science photos, can detect insects in similar images with high accuracy, but their p...

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Bibliographic Details
Main Authors: Valentin Ștefan, Thomas Stark, Michael Wurm, Hannes Taubenböck, Tiffany M. Knight
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
Subjects:
Pollinator detection
Automated insect monitoring
Out-of-distribution generalisation
YOLO detectors
Smartphone images
Time-lapse images
Online Access:https://doi.org/10.1038/s41598-025-16140-z
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https://doi.org/10.1038/s41598-025-16140-z

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