AUTHOR=Mills Ben , Zervas Michalis N. , Grant-Jacob James A. 

TITLE=Pollen image manipulation and projection using latent space

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1539128

DOI=10.3389/fpls.2025.1539128

ISSN=1664-462X

ABSTRACT=Understanding the structure of pollen grains is crucial for the identification of plant taxa and the understanding of plant evolution. We employ a deep learning technique known as style transfer to investigate the manipulation of microscope images of these pollens to change the size and shape of pollen grain images. This methodology unveils the potential to identify distinctive structural features of pollen grains and decipher correlations, whilst the ability to generate images of pollen can enhance our capacity to analyse a larger variety of pollen types, thereby broadening our understanding of plant ecology. This could potentially lead to advancements in fields such as agriculture, botany, and climate science.