| Abstrakt: |
Mango disease management is a critical challenge for mango growers, and an intelligent system that can effectively diagnose and treat diseases is highly desirable. In this article, we propose an innovative approach that integrates case-based reasoning (CBR) and rule-based reasoning (RBR) using data mining techniques. The data source consists of real-world data collected from the Ethiopian Agricultural Research Institute and the National Meteorology Agency. We employ CommonKADs knowledge representation techniques to model rules in rule-based reasoning and utilize case similarity for the representation of cases in case-based reasoning. Additionally, we compare data mining algorithms with an ensemble approach, where J-48 Boosting emerges as the best-performing algorithm with an accuracy of 86.26%. The integration of CBR and RBR is achieved using rule-dominant approaches, which significantly enhance the system's accuracy and efficiency. The extracted rules and represented cases are integrated to develop the Decision support system and the system is developed using Java programming languages. The system has been evaluated to ensure the performance of the system is accurate and is the system usable by the researcher and development agent. The system has registered an overall performance of 94.25% accuracy in both system performance and user acceptance testing. Hence, this study concludes that the integration of rule-based and case-based reasoning approaches achieves better performance concerning the performance of individual reasoning approaches in the identification, recommending first-line treatment, and prevention of Mango infection. The finding of this study can be used as a supportive tool for agricultural extension workers, farmers, and farmworkers to help in the diagnosis and treatment of mango disease. |
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