Pollen analysis using multispectral imaging flow cytometry and deep learning

Abstract

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 multispectral 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.

Efficient, automated and robust pollen analysis using deep learning

“…Recent studies have shown accuracies close to 100% (Bourel et al., 2020; Daood et al., 2016; Dunker et al., 2020; Sevillano & Aznarte, 2018), and even for a severe problem with 46 pollen types, Sevillano et al. (2020) arrived at a correct classification rate of nearly 98%.…”

“…Although significant progress has been made towards automated pollen analysis during recent years (e.g. Bourel et al., 2020; Dunker et al., 2020; Holt & Bennett, 2014; Sevillano et al., 2020), many field studies are still constrained by laborious and costly pollen identification and quantification.…”

“…Since then, several new studies with promising results have been published (e.g. Bourel et al., 2020; Daood et al., 2016; Dunker et al., 2020; Sevillano & Aznarte, 2018; Sevillano et al., 2020). This illustrates the general progress made in the field of machine learning and artificial intelligence, especially using deep neural networks, which are well suited for classifying two‐dimensional images (Sevillano & Aznarte, 2018).…”

Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

“…Over the past few years, several studies using machinelearning algorithms have been conducted to monitoring of the airborne pollen or to develop automatic classification system of pollen grains [19], [20], [21]. There are also many studies that have utilized CNN models to classify various pollen species [22], [23], [24], [25], [26], [27]. However, our research is focused on establishing a reliable method for assessing pollen viability using germination images rather than identifying the pollen species covered in the palynology fields.…”

Deep learning in deep time

“…This hierarchical interpretation of visual cues is also not unlike the way a traditional taxonomist might work to classify an organism, and deep learning is now being applied widely for automated species detection and ecosystem monitoring (reviewed in ref. 14 ) and was recently developed in a study of extant pollen ( 15 ). Romero et al ( 9 ) show how CNNs trained to identify modern taxa can be paired with imaging techniques to classify organisms deep in the fossil record.…”