  Oceans are the essential lifeblood of the Earth: they provide over 70% of the
oxygen and over 97% of the water. Plankton and corals are two of the most
fundamental components of ocean ecosystems, the former due to their function at
many levels of the oceans food chain, the latter because they provide spawning
and nursery grounds to many fish populations. Studying and monitoring plankton
distribution and coral reefs is vital for environment protection. In the last
years there has been a massive proliferation of digital imagery for the
monitoring of underwater ecosystems and much research is concentrated on the
automated recognition of plankton and corals. In this paper, we present a study
about an automated system for monitoring of underwater ecosystems. The system
here proposed is based on the fusion of different deep learning methods. We
study how to create an ensemble based of different CNN models, fine tuned on
several datasets with the aim of exploiting their diversity. The aim of our
study is to experiment the possibility of fine-tuning pretrained CNN for
underwater imagery analysis, the opportunity of using different datasets for
pretraining models, the possibility to design an ensemble using the same
architecture with small variations in the training procedure. The experimental
results are very encouraging, our experiments performed on 5 well-knowns
datasets (3 plankton and 2 coral datasets) show that the fusion of such
different CNN models in a heterogeneous ensemble grants a substantial
performance improvement with respect to other state-of-the-art approaches in
all the tested problems. One of the main contributions of this work is a wide
experimental evaluation of famous CNN architectures to report performance of
both single CNN and ensemble of CNNs in different problems. Moreover, we show
how to create an ensemble which improves the performance of the best single
model.
