Machine learning methods for acoustic-based automatic Posidonia meadows detection by means of unmanned marine vehicles

This work describes the exploitation of a Remotely Operated Vehicle (ROV), equipped with a multi-parametric sensors package (acoustic and video), for the exploration and characterisation of sea-bottoms covered with Posidonia oceanica seagrass, which represents a valuable indicator of the environmental health. The data collection is achieved by the employment of a single beam echosounder and a down-looking underwater camera. An acoustic data procedural analysis based on machine learning methods was developed to automatically detect the Posidonia presence, so that in future works it will be possible to operate also in low-visibility conditions, using only the acoustic sensors. Data acquisition was carried out over different seafloor types in coastal area near Biograd Na Moru (Croatia) and the preliminary results are reported in the paper.