A freshwater algae classification system based on machine learning with StyleGAN2-ADA augmentation for limited and imbalanced datasets.

Automated algae classification using machine learning is a more efficient and effective solution compared to manual classification, which can be tedious and time-consuming. However, the practical application of such a classification approach is restricted by the scarcity of labeled freshwater algae datasets, especially for rarer algae. To overcome these challenges, this study proposes to generate artificial algal images with StyleGAN2-ADA and use both the generated and real images to train machine-learning-driven algae classification models. This approach significantly enhances the performance of classification models, particularly in their ability to identify rare algae. Overall, the proposed approach improves the F1-score of lightweight MobileNetV3 classification models covering all 20 freshwater algae covered in this research from 88.4% to 96.2%, while for the models that cover only the rarer algae, the experiments show an improvement from 80% to 96.5% in terms of F1-score. The results show that the approach enables the trained algae classification systems to effectively cover algae with limited image data.