Case-Based Reasoning in identifying causes of fish death in industrial fish farming

Abstract

Fish farming is a million dollar business world wide, and fish is in fact the third mostimportant export product after oil/gas and metal in Norway. There are a lot of different aquaculture sites which produce fish along our long coast line and they all have somedifferences in the production rates and procedures. The fish farmer at these sites holdvaluable information about the production, which is almost impossible to derive onlyfrom empirical data.In this thesis we introduce Glaucus, a Case-Based Reasoning system which aims tohelp the fish farmers with their decision making when conduction sorting operations attheir aquaculture sites. The system is built in Java and uses the jColibri developmentframework for Case-Based Reasoning. It retrieves cases based on similarity function frommyCBR and jColibri in addition to custom made ones. The case base is generated fromreal world data and the case queries are populated by a combination of user input anddata from a database with continuous data flow.Our approach is just the beginning of what we hope will be a even greater journeytowards a complete decision support system that will meet the expectations of the fishfarmers.Keywords: Case-Based Reasoning, Machine learning, Fish farming, jColibri, myCBR