<p>Plankton communities form the basis of aquatic ecosystems and elucidating their role in increasingly important environmental issues is a constantly present research question. The concealed plankton community dynamics reflect changes in environmental forcing, growth traits of competing species, and multiple food web interactions. Recent technological advances have led to the possibility of collecting real-time big data opening new horizons for testing core hypotheses in planktonic systems, derived from macroscopic realms, in community ecology, biodiversity research, and ecosystem functioning. Analyzing the big data calls for computer vision and machine learning methods capable of producing interoperable data across platforms and systems. In this paper we apply convolutional neural networks (CNN) to classify a brackish-water phytoplankton community in the Baltic Sea. For solving the classification task, we utilize compact CNN architectures requiring less computational capacity and creating an opportunity to quickly train the network. This makes it possible to (1) test various modifications to the classification method, and (2) repeat each experiment multiple times with different training and test set combinations to obtain reliable results. We further analyze the effect of large class imbalance to the CNN performance, and test relevant data augmentation techniques to improve the performance. Finally, we address the practical implications of the classification performance to aquatic research by analyzing the confused classes and their effect on the reliability of the automatic plankton recognition system, to guide further development of plankton recognition research. Our results show that it is possible to obtain good classification accuracy with relatively shallow architectures and a small amount of training data when using effective data augmentation methods even with a very unbalanced dataset.</p>