Phytoplankton identification with prototypical networks: A few-shot learning approach

Abstract

The recognition of phytoplankton in microscopy images remains a challenging task due, among other factors, to the vast diversity of known species and the limited availability of labeled training data. Recent advances in pattern recognition have facilitated the automation of this process, offering experts tools to reduce annotation time and increase classification reliability. However, the core difficulty persists, traditional models struggle with unseen species and data scarcity. This study presents a novel application of Prototypical Networks for the automatic recognition of cyanobacteria and diatoms, a method not previously applied to this domain, to the best of our knowledge. Our approach addresses a critical limitation of conventional classifiers by enabling the integration of new, previously unseen species into the recognition framework. To this end, data balancing and augmentation techniques based on deep learning were applied, followed by the training of detection and classification models using Few-Shot Learning, with a focus on Prototypical Networks. The results demonstrate the model's ability to incorporate novel cyanobacteria and diatom genera with minimal annotated data, offering a promising solution for biodiversity monitoring and environmental assessment.