94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
HYPOTHESIS AND THEORY article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1539128
Pollen image manipulation and projection using latent space
Provisionally accepted- University of Southampton, Southampton, United Kingdom
Understanding the structure of pollen grains is crucial for the identification of plant taxa and understanding plant evolution. We employ a deep learning technique known as style transfer to investigate the manipulation of microscope images of these pollen to change the size and shape of pollen grains 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.
Keywords: Pollen, Latent space, deep learning, evolution, imaging
Received: 03 Dec 2024; Accepted: 06 Feb 2025.
Copyright: © 2025 Mills, Zervas and Grant-Jacob. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
James Andrew Grant-Jacob, University of Southampton, Southampton, United Kingdom
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.