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Use of data science and machine learning techniques for study Sea lice (Caligus rogercresseyi) infestation on Atlantic salmon (Salmo salar)
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P. De los Ríos-Escalante *   , E. Ibáñez-Arancibia |
| Departamento de Ciencias Biológicas y Químicas, Facultad de Recursos Naturales, Universidad Católica de Temuco, Temuco, Chile |
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Abstract: (1000 Views) |
| Salmon farming, mainly Atlantic salmon (Salmo salar) is the main productive activity in Chilean Patagonia (38-53°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 to analyze the infestation rate of sea lice Caligus rogercresseyi on Salmo salar farmed in the Aysen region in central Chilean Patagonia (43-50° S). The results revealed the existence of a weak but not significant 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 of Caligus rogercresseyi infestation on Chilean farmed salmonids. |
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| Keywords: Salmo salar, Caligus rogercresseyi, Machine learning, Parasites, Chile |
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Full-Text [PDF 138 kb]
(175 Downloads)
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Type of Study: Research |
Subject:
General
Received: 2024/02/24 | Published: 2024/01/30
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