LAUSR

The expert system approach, although quite old, is still quite effective in scientific areas where experts are required to make diagnoses and predictions. One of those areas is fish disease diagnosis. It is an application domain that currently employs complicated processes, which require high level skills for making accurate diagnoses. On the other hand, complete datasets for full diagnosis to be able to use machine learning techniques are not available. Therefore, in aquaculture, now more than ever, fish farmers do not have the required expertise or equipment to accurately diagnose a fish disease. For that reason, expert systems that can help in the diagnosis, prevention, and treatment of diseases have been developed. In this paper, we attempt to give an overview of the expert system approaches for fish disease diagnosis developed in the last two decades. Based on the analysis of their technical and non-technical characteristics, we propose an expert system architecture and a fish disease diagnosis process aiming at improving the deficiencies of the existing systems. The proposed system can handle all types of fish diseases based on image and non-image data as well as on molecular test results and can provide explanations. The diagnosis process goes through four consecutive levels, where each next level considers an additional category of parameters and provides diagnoses with a higher certainty.