Toward phytoplankton parasite detection using autoencoders

Phytoplankton parasites are largely understudied microbial components with a potentially significant ecological influence on

phytoplankton bloom dynamics. To better understand the impact of phytoplankton parasites, improved detection methods are

needed to integrate phytoplankton parasite interactions into monitoring of aquatic ecosystems. Automated imaging devices

commonly produce vast amounts of phytoplankton image data, but the occurrence of anomalous phytoplankton data in such

datasets is rare. Thus, we propose an unsupervised anomaly detection system based on the similarity between the original and

autoencoder-reconstructed samples.With this approach, we were able to reach an overall F1 score of 0.75 in nine phytoplankton

species, which could be further improved by species-specific fine-tuning. The proposed unsupervised approach was further

compared with the supervised Faster R-CNN-based object detector. Using this supervised approach and the model trained

on plankton species and anomalies, we were able to reach a highest F1 score of 0.86. However, the unsupervised approach is

expected to be more universal as it can also detect unknown anomalies and it does not require any annotated anomalous data

that may not always be available in sufficient quantities. Although other studies have dealt with plankton anomaly detection

in terms of non-plankton particles or air bubble detection, our paper is, according to our best knowledge, the first that focuses

on automated anomaly detection considering putative phytoplankton parasites or infections.