  A disconcerting ramification of water pollution caused by burgeoning
populations, rapid industrialization and modernization of agriculture, has been
the exponential increase in the incidence of algal growth across the globe.
Harmful algal blooms (HABs) have devastated fisheries, contaminated drinking
water and killed livestock, resulting in economic losses to the tune of
millions of dollars. Therefore, it is important to constantly monitor water
bodies and identify any algae build-up so that prompt action against its
accumulation can be taken and the harmful consequences can be avoided. In this
paper, we propose a computer vision system based on deep learning for algae
monitoring. The proposed system is fast, accurate and cheap, and it can be
installed on any robotic platforms such as USVs and UAVs for autonomous algae
monitoring. The experimental results demonstrate that the proposed system can
detect algae in distinct environments regardless of the underlying hardware
with high accuracy and in real time.
