Expert system for diagnosing vaname shrimp disease using forward chaining and case-based reasoning methods

Abstract

An important part of the fishing industry is the maintenance of shrimp health, especially when it comes to the highly valuable Vannamei shrimp that are cultivated. However, illnesses and other health problems frequently cause productivity problems and result in losses for shrimp growers. The creation of an expert system has been suggested as a potential remedy to these problems. The goal of this research is to create an expert system that can diagnose diseases in shrimp quickly and accurately by analyzing clinical symptoms. This expert system was developed by applying artificial intelligence algorithms for data analysis and diagnosis, recognizing clinical symptoms of shrimp diseases, and learning from fishery professionals. Additionally, the system offers specialized disease control options made to fit the environmental requirements of shrimp farming. This expert system has proven its ability to provide precise diagnoses and suitable treatments for a range of shrimp ailments through testing and evaluation at many Vannamei shrimp farming sites. Improved efficacy and efficiency in managing shrimp health are among the ramifications of this research, which can lower financial losses and promote the expansion of the sustainable fisheries sector.