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AI Rule-Based Expert System: Diagnosis and Treatment of Bean Diseases
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This study introduces an AI rule-based expert system designed to help diagnose and treat common bean diseases. Built with SWI-Prolog, the system uses if-then rules to analyze symptoms provided by users, identify the most likely disease, and recommend suitable treatments. Its structure combines a knowledge base, inference engine, and rule set that cover major diseases such as Fusarium Wilt, Charcoal Rot, Leaf Spot, Mung Bean Yellow Mosaic, and Cercospora Leaf Spot. Tests and expert reviews confirmed that the system is accurate, consistent, and reliable, making it a practical decision-support tool for farmers and agricultural workers. By automating diagnosis, it reduces dependence on human experts, enables quicker disease management, and helps farmers prevent crop losses. The study also shows how SWI-Prolog supports clear and logical reasoning in agricultural applications. Future enhancements include expanding the knowledge base, improving the user interface, and linking the system with mobile or database platforms for wider access.
International Research Publication and Journals
Title: AI Rule-Based Expert System: Diagnosis and Treatment of Bean Diseases
Description:
This study introduces an AI rule-based expert system designed to help diagnose and treat common bean diseases.
Built with SWI-Prolog, the system uses if-then rules to analyze symptoms provided by users, identify the most likely disease, and recommend suitable treatments.
Its structure combines a knowledge base, inference engine, and rule set that cover major diseases such as Fusarium Wilt, Charcoal Rot, Leaf Spot, Mung Bean Yellow Mosaic, and Cercospora Leaf Spot.
Tests and expert reviews confirmed that the system is accurate, consistent, and reliable, making it a practical decision-support tool for farmers and agricultural workers.
By automating diagnosis, it reduces dependence on human experts, enables quicker disease management, and helps farmers prevent crop losses.
The study also shows how SWI-Prolog supports clear and logical reasoning in agricultural applications.
Future enhancements include expanding the knowledge base, improving the user interface, and linking the system with mobile or database platforms for wider access.
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