Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1038/s41598-023-43482-3
Licence creative commons licence
Title (Primary) YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images
Author Stark, T.; Ştefan, V.; Wurm, M.; Spanier, R.; Taubenböck, H.; Knight, T.M.
Source Titel Scientific Reports
Year 2023
Department BZF; iDiv
Volume 13
Page From art. 16364
Language englisch
Topic T5 Future Landscapes
Supplements https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-023-43482-3/MediaObjects/41598_2023_43482_MOESM1_ESM.pdf
Keywords Biodiversity; Population dynamics
Abstract Develoment of image recognition AI algorithms for flower-visiting arthropods has the potential to revolutionize the way we monitor pollinators. Ecologists need light-weight models that can be deployed in a field setting and can classify with high accuracy. We tested the performance of three deep learning light-weight models, YOLOv5nano, YOLOv5small, and YOLOv7tiny, at object recognition and classification in real time on eight groups of flower-visiting arthropods using open-source image data. These eight groups contained four orders of insects that are known to perform the majority of pollination services in Europe (Hymenoptera, Diptera, Coleoptera, Lepidoptera) as well as other arthropod groups that can be seen on flowers but are not typically considered pollinators (e.g., spiders-Araneae). All three models had high accuracy, ranging from 93 to 97%. Intersection over union (IoU) depended on the relative area of the bounding box, and the models performed best when a single arthropod comprised a large portion of the image and worst when multiple small arthropods were together in a single image. The model could accurately distinguish flies in the family Syrphidae from the Hymenoptera that they are known to mimic. These results reveal the capability of existing YOLO models to contribute to pollination monitoring.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=28023
Stark, T., Ştefan, V., Wurm, M., Spanier, R., Taubenböck, H., Knight, T.M. (2023):
YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images
Sci. Rep. 13 , art. 16364