Knowledge of the vertical migration pattern of sea lice (Lepeophtheirus salmonis) copepodites is necessary for designing efficient measures to prevent lice infestations on farmed Atlantic salmon (Salmo Salar) in sea-cages. However, data can be challenging to acquire at a large scale under realistic circumstances without interfering with the natural behavior of the specimen. A mesocosm platform was built to help acquire this data consisting of a sensor package in an underwater housing being pulled up and down along a 11-meter-long transparent tube containing planktonic organisms while collecting image-, temperature- and spectrometer data. The platform was placed at a salmon farm and the acrylic tube was filled with L. salmonis copepodites and was pre-programmed to run a profile scan twice per hour for four consecutive days. Using a fully convolutional neural network, the copepodites were automatically counted – creating a depth profile of detected lice and measured light specter. The final results showed a diurnal migration pattern throughout the test period.•Capable of acquiring vertical density profiles of any aquatic species between 0,5 – 10 mm down to 11 m below the surface.•Fully automated and can be left unintended for weeks while collecting data.