Developing a Knowledge Based System for Coffee Disease Diagnosis and Treatment
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Date
2012-06
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Addis Ababa University
Abstract
Arabica coffee (Coffea arabica L.) is the most important cash crop that has been contributing a great share to the Ethiopian economy. Although, it plays a significant role in the economy of the country, the crop suffers from many production constraints like diseases and pests. Detecting those diseases and pests at early stages enable us to overcome and treat them appropriately. This process requires an expert to identify the disease and pests, describe the methods of treatment and protection, but the expert is not always available to be referred. Expert systems help a great deal in identifying those diseases and pests and describing methods of treatment to be carried out. By taking into account such advantages of expert systems, this paper presents a knowledge based system (KBS) in the area of agriculture and describes the design and the development of the rule based system, using prolog programming language. It focuses on the development of KBS for coffee disease and pest control where it is intended for the diagnosis of common diseases and pests occurring in the coffee plant. An expert system is a computer program normally composed of a knowledge base, inference engine and user interface and the proposed system basically composed of these components. The system integrates a structured knowledge base that contains knowledge about symptoms and medication of diseases and pests in the coffee plant appearing during their life span. Agricultural officers and planters who involve directly with coffee plantation may use this system as an assistant for helping them in managing the crop activities especially in diseases and pest control. For development purposes, knowledge engineering methodology was selected as a guide. Perhaps, this system may become the most popular alternative for performing and work as an assistant to produce a better quality of coffee product. The system was evaluated using visual interactive method; it was shown that the system agreed with human expert opinions in 83.6 percent of the decision.
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Knowledge Based System