Efficient and scalable training set generation for automated pollen monitoring with Hirst-type samplers.

ABSTRACT: Automated pollen detection is essential for ecological monitoring, allergy forecasting, and biodiversity research. However, existing methods rely heavily on manual or semi-automated annotations, limiting scalability and broader applicability. We introduce a highly automated training dataset generation pipeline that combines one-shot detection with systematic refinement, producing tens of thousands of high-quality annotations from bright-field microscopy while significantly reducing manual effort and annotation costs. Using multi-regional datasets from France, Hungary, and Sweden, we trained object detection models on seven pollen taxa and evaluated their performance on both external pure and mixed species slides and real-world airborne samples. We assessed the reusability of pretrained vis

SUBMITTER: Biricz A 

PROVIDER: S-EPMC12808117 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature