Tobacco plants have high economic value, but their productivity is often threatened by various diseases that can harm farmers. Lack of knowledge in diagnosing diseases accurately and limited access to agricultural experts are major obstacles in controlling diseases. To overcome this problem, an expert system based on the forward chaining method was developed that is able to diagnose tobacco plant diseases based on observed symptoms. This system matches symptoms with rules in the knowledge base to produce an accurate diagnosis. The test results showed that the system had 100% accuracy on the five tobacco plant data sets tested. This success shows the potential of expert systems as an effective tool to increase tobacco plant productivity and facilitate decision making in the field.
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