The Underwater Vision Profiler or UVP (CNRS patent) is designed to study large (>100 µm) particles and zooplankton simultaneously and to quantify them in a known volume of water. The UVP system makes use of computerised optical technology with custom lighting to acquire digital images of zooplankton IN SITU down to depths of 6000m.
The UVP6-LP is a miniaturized and low price version of the UVP5. It has been designed for low speed, limited space and low power vectors like profiling floats, gliders, floats, moorings, AUVs…
It has been designed with the same quality standards than the UVP5 and provide an even higher resolution of 5Mp. It acquires only in-focus images in a volume of water delimited by a single red flashing light illuminating a volume of 0.65 Litre. The UVP6-LP are inter-calibrated with UVP5std and UVP5hd systems.
The UVP6-LP can transmit real time particle abundances to the vector and records vignettes of selected organisms and large aggregates as the UVP5 do. A future version will provide also an embedded classification of the images.
Warning: the UVP6-LP cannot be utilized on CTD rosette due to its 1.3Hz low acquisition frequency and 500µS flashes.

This sensor was developed in the frame of BRIDGES Program.

The Underwater Vision Profiler or UVP (CNRS patent) is designed to study large (>100 µm) particles and zooplankton simultaneously and to quantify them in a known volume of water. The UVP system makes use of computerised optical technology with custom lighting to acquire digital images of zooplankton IN SITU down to depths of 6000m.

The ZooSCAN (CNRS patent) system makes use of scanner technology with custom lighting and a watertight scanning chamber into which liquid zooplankton samples can be placed. The scanner recovers a high-resolution, digitial image and the sample can be recovered without damage. (You can download an example image exactly as it was recovered here.) These digital images can then be investigated by computer processing.While the resolution of the digitized zooplankton images is lower than the image obtained using a binocular microscope this technique has proved to be more than adequate for large sample sets. Identification of species is done by automatic comparison of the image (vignette) of each individual animal in the scanned image with a library data set which may be built by the investigator for each individual survey or imported from a previous survey. The latest machine learning algorithm allows high recognition levels even if we recommend complementary manual sorting to achieve a high number of taxonomic groups (see JPR ZooSCAN article.)