Research of Litchi Diseases Diagnosis Expertsystem Based on Rbr and Cbr

  • Bing Xu
  • Liqun Liu
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 293)


To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.


Association Rule Disease Diagnosis Decision Tree Algorithm Target Case Diagnosis Result 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.College of Information TechnologyGuangdong Ocean UniversityZhanjiangP.R. China
  2. 2.Ministry of PersonnelGuangdong Ocean UniversityZhanjiangP.R.China
  3. 3.College of Information TechnologyGuangdong Ocean UniversityZhanjiangP. R. China

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