|DOI / URL||link|
|Title (Primary)||Pollen analysis using multispectral imaging flow cytometry and deep learning|
|Author||Dunker, S.; Motivans, E.; Rakosy, D.; Boho, D.; Mäder, P.; Hornick, T.; Knight, T.;|
|Department||BZF; iDiv; PHYDIV;|
|POF III (all)||T11;|
|Keywords||Convolutional neural networks; Deep learning; multispectral imaging flow cytometry; pollen; pollinator; species identification|
Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard.
In this study, we present a new method for pollen analysis using multi‐spectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426,876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best‐performing classifier to yield a species‐averaged accuracy of 96 %. Even species that are difficult to differentiate using microscopy could be clearly separated.
Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification, and trait extraction of recent pollen.
|Persistent UFZ Identifier||https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=23548|
|Dunker, S., Motivans, E., Rakosy, D., Boho, D., Mäder, P., Hornick, T., Knight, T. (2021):
Pollen analysis using multispectral imaging flow cytometry and deep learning
New Phytol. 229 (1), 593 - 606