Automated gelatinous zooplankton acquisition and recognition

Much is still unknown about marine plankton abundance and dynamics in the open and interior ocean. Especially challenging is the knowledge of gelatinous zooplankton distribution, since it has a very fragile structure and cannot be directly sampled using traditional net based techniques. In the last decades there has been an increasing interest in the oceanographic community toward imaging systems. In this paper the performance of three diffierent methodologies, Tikhonov regulariza- tion, Support Vector Machines, and Genetic Programming, are analyzed for the recognition of gelatinous zooplankton. The three methods have been tested on images acquired in the Ligurian Sea by a low cost under- water standalone system (GUARD1). The results indicate that the three methods provide gelatinous zooplankton identication with high accu- racy showing a good capability in robustly selecting relevant features, thus avoiding computational-consuming preprocessing stages. These aspects fit the requirements for running on an autonomous imaging system designed for long lasting deployments.