ArticleScientific reports2023
YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Article
- Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds.Journal of insect science (Online) · 2026Article
- Major Depressive Disorder Signatures: A Review of Artificial Intelligence (AI)-Powered Insights Into Gut Dysbiosis.Health science reports · 2026Article
- Integrating morphological and deep learning approaches for the identification of economically important nematode genera in vineyards:Helminthologia · 2026Article
- Digital Monitoring and Modeling of Honey Bee Foraging on Clover Flowers Under Heat Stress.microPublication biology · 2026Article
- Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring.Scientific reports · 2025Article
- Hatching-Box: Automated in situ monitoring of Drosophila melanogaster development in standard rearing vials.PloS one · 2025Article
- Utilizing CNNs for classification and uncertainty quantification for 15 families of European fly pollinators.PloS one · 2025Article
- Human Gut Microbiota for Diagnosis and Treatment of Depression.International journal of molecular sciences · 2024Review
- Deep learning-based image classification of sea turtles using object detection and instance segmentation models.PloS one · 2024Article
- Article
- Unveiling the Connection between Microbiota and Depressive Disorder through Machine Learning.International journal of molecular sciences · 2023Article
- Effectiveness of interval photography cameras for a survey of pollinator communities: Comparison with direct observation.Applications in plant sciencesArticle
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6 authors.
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No grant is acknowledged in the PubMed record.
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.
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