Design And Realization Of The Expert System For Prawn Disease Diagnose

Prawn farming plays a very important role in aquaculture. With the rapid development of computer technology, information technology and network technology, the direction of aquaculture also develop toward informationalized breeding approach from the very beginning of the traditional breeding approach and prawn farming is no exception. Currently, a huge economic loss can be ascribed to prawn disease under the background of modernization, intensification and densification of aquaculture. The disease has become the largest concern of farmers and also restricted the development of aquaculture. Emergence of prawn disease has the following figures which include explosive spread, difficulty of controlling, high mortality and strong infectivity. So, how to conduct effective prevention, timely diagnosis and treatment is vitally significant.Recently, research on utilizing artificial intelligence technology to diagnosis of aquatic animal diseases has developed rapidly. However, study on expert system for prawn disease has been reported rarely. And research on diagnosis of prawn by combining BP neural network with B/S three layers architecture model has never been reported yet. In this thesis, expert system designed and realized for diagnosis of prawn disease is a subsystem of crustacean aquaculture expert system. We have made a analysis about issues related to prawn disease diagnosis, united with expert system based on neural network applications in the field of disease diagnosis and applied BP neural network algorithm theory, expert system theory, database storage technology,ASP.NET technology and B/S three-tier architecture of ideas to design and code to realize. In this thesis, we first introduce the artificial neural network theory, BP algorithm reasoning process and integrated theory of neural network expert system,and then collect information related prawn diseases to analyze and process, including prawn disease types, corresponding symptoms and ways of thinking to solve problems,design appropriate expert system model of prawn disease diagnosis, and finally use.NET technologies to code and realize, complete the diagnostic function of prawn diseases, and ultimately get more accurate results of diagnosis. The use of BP neural network makes the system have ability of learning, self-adaption, parallel reasoningand associative memory. Furthermore, when the system is under B/S three layers architecture model, the user can simply log in through the browser. It’s user-friendly,simple and practical and easy to maintain and has a certain practical significance and promotional value.