| Summary: | International audience In recent years, autonomous platforms have been improved with the objective of observing the deep ocean. Many prototypes have reached 6,000 m, but only a few are commercially available. Imaging sensors have been developed to study the numbers, sizes and shapes of particles and plankton. These have been deployed independently or mounted on CTD rosettes (e.g. the UVP5 Underwater Vision Profiler). In some areas, they observed high concentrations of large particles down to the bottom of the ocean, showing intense carbon export. They also documented the presence of fragile organisms, such as rhizarians, whose key ecological roles in the ocean was unknown until then. The new miniaturized UVP6-LP (Low Power) sensor, developed to be mounted on autonomous platforms, is complementary to the larger sensors deployed on CTD rosettes. It also records and identifies particles and plankton, using imagery. It counts and sizes particles \textgreater80 µM ESD and identifies large aggregates and plankton \textgreater700µM ESD. This, at low cost and with very low power. Six prototypes of the sensor have been inter-calibrated with the reference UVP5. The instrument can be deployed down to 6,000 m, which corresponds to 97% of the ocean surface. A UVP6-LP was used to quantify particles released from a deep-sea mining experiment at 4,300 m. Others performed transects on gliders and vertical profiles on a float. A UVP6-LP was moored for one year at 50 m at 82°N. Other UVP6-LP will be deployed on the a cabled observatory in the Mediterranean Sea at 2,200 m, and on the SeaCycler mooring in the Labrador Sea. The very low power required for the operation of the UVP6-LP allows to optimize its use on profiling floats and gliders. Autonomous platforms cannot transmit images due to the limitation of satellite bandwidth or acoustic telemetry. To overcome this limitation, the UVP6-LP includes an embedded algorithm for the automatic classification of large aggregates and plankton images, which provides data that are ...
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