Use of data science and machine learning techniques for study Sea lice (Caligus rogercresseyi) infestation on Atlantic salmon (Salmo salar)

The Salmon farming, mainly Atlantic salmon (Salmo salar) is the main productive activity in Chilean Patagonia (۳۸-۵۳°S), one of the main problems for Salmo salar farming, is the infestation of sea lice Caligus rogercresseyi. The aim of the present study was analyzed the infestation rate of sea lice Caligus rogercresseyi on Salmo salar farmed in Aysen region in central Chilean Patagonia (۴۳-۵۰° S). The results revealed the existence of a weak but not significative relation between latitude and infestation rate, whereas it was found inverse direct associations between temperature and salinity with infestation rate. The possible cause would be due in Southern latitudes, the temperature and salinity decrease, that are conditions that limit the infestation rate of Caligus rogercresseyi on Salmo salar in Southern Chile. The exposed results would be similar with literature descriptions, and would indicate that use of data science and machine learning can be a powerful tool for study Caligus rogercresseyi infestation on Chilean farmed salmonids.