 Abstract plant and microorganisms play a huge role in the aquatic food web. Recently, it has been proposed to use plantin as a biosensor, since they can react to even minimal perturbations of the aquatic environment with specific physiological changes, which may lead to alterations in morphology and behavior. Nowadays, the development of High Resolution Institute Automatic Acquisition Systems allows the research community to obtain a large amount of plantin image data. Fundamental examples are the ZooScan and Woods Hole Oceanographic Institution, WHOI, datasets, comprising up to millions of plantin images. However, obtaining unbiased annotations is expensive both in terms of time and resources, and institute acquired datasets generally suffer from severe imbalance, with only a few images available for several species. Transfer learning is a popular solution to these challenges, with ImageNet 1k being the most used source dataset for pre-training. On the other hand, datasets like the ZooScan and the WHOI may represent a valuable opportunity to compare out-of-domain and large-scale plantin in-domain source datasets, in terms of performance for that. This article was authored by Andrea Maracani, Vito Paolo Pastor, Lorenzo Notale, and others. We are article.tv, links in the description below.