Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.

ABSTRACT: Pollen and tracheophyte spores are ubiquitous environmental indicators at local and global scales. Palynology is typically performed manually by microscopic analysis; a specialised and time-consuming task limited in taxonomical precision and sampling frequency, therefore restricting data quality used to inform climate change and pollen forecasting models. We build on the growing work using AI (artificial intelligence) for automated pollen classification to design a flexible network that can deal with the uncertainty of broad-scale environmental applications. We combined imaging flow cytometry with Guided Deep Learning to identify and accurately categorise pollen in environmental samples; here, pollen grains captured within c. 5500 Cal yr BP old lake sediments. Our network discriminates not

SUBMITTER: Barnes CM 

PROVIDER: S-EPMC10594409 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature