AI facilitated fluoro-electrochemical phytoplankton classification

Abstract

Marine phytoplankton is extremely diverse. Counting and characterising phytoplankton is essential for understanding climate change and ocean health not least since phytoplankton extensively biomineralize carbon dioxide whilst generating 50% of the planet's oxygen. We report the use of fluoro-electrochemical microscopy to distinguish different taxonomies of phytoplankton by the quenching of their chlorophyll-a fluorescence using chemical species oxidatively electrogenerated in situ in seawater. The rate of chlorophyll-a quenching of each cell is characteristic of the species-specific structural composition and cellular content. But with increasing diversity and extent of phytoplankton species under study, human interpretation and distinction of the resulting fluorescence transients becomes increasingly and prohibitively difficult. Thus, we further report a neural network to analyse these fluorescence transients, with an accuracy >95% classifying 29 phytoplankton strains to their taxonomic orders. This method transcends the state-of-the-art. The success of the fluoro-electrochemical microscopy combined with AI provides a novel, flexible and highly granular solution to phytoplankton classification and is adaptable for autonomous ocean monitoring.

Fig. 1. Schematic illustration of the workflow of fluoro-electrochemical classification. (a) Setup of fluoro-electrochemical microscopy and (b) fluorescence transients captured and analysed by a neural network (c) to predict the taxonomic order of the testing dataset. (d) The classification of test sample. From P1 to P4 are probabilities of an isochrysidales, hemiaulales, dinophysiales or Coscinodiscus (images can be found in World Wide Web of Plankton Image Curation, https://ecotaxa.obs-vlfr.fr/).

Fig. 2. Microscopic images for each of the 29 strains randomly drawn from the dataset of 3325 images. The images were taken at 20 times magnification. The title for each image represents the ID of each strain with the taxonomic order of each strain. The corresponding strain names are shown in Fig. 3.

Fig. 3. The normalized fluorescence quenching transients of each species from 0 to 19 seconds randomly drawn from the dataset of 2911 transients.

Fig. 4. The structure of neural network designed to classify phytoplankton using fluorescence transients and radii. k, w and r are kernel size, window width and dropout rate for convolution, max pooling and dropout layers, respectively.

Fig. 5. Normalized confusion matrix of phytoplankton classification using (a) transfer learning on images with ResNet50V2, (b) plots 500 phytoplankton samples by their t_1/2 and radii and the scatter colour represent the taxonomic orders of the species, (c) KNN classifier using half-lives (t_1/2) and radii, and (d) neural network classification using fluorescence transients and radii.

Fig. 6. Confusion matrix of testing 1D Inception network with unseen E. huxleyi and interference strains. The interference strains for (a) were Phaeodactylum tricornutum (diatom, ID = 1), Minidiscus variabilis (diatom, ID = 25) and Scripsiella trochoidea (dinoflagellates, ID = 27), and for (b) was Gephyrocapsa oceanica (ID = 18).