Integrating certainty factor into a web-based expert system for intelligent diagnosis of chili plant pathologies

Chili pepper plant diseases are a major problem that can reduce yield and quality. Farmers face difficulties in diagnosing chili plant diseases due to limited knowledge and resources. This study presents a web-based expert system that integrates the Certainty Factor (CF) method for intelligent diagnosis of chili plant diseases. This system was built to assist farmers and agricultural practitioners in identifying common chili plant pathologies through a user-friendly web interface. The system was evaluated through validation by domain experts and testing with real-world symptom cases, resulting in high accuracy and reliability. The system was tested and validated by experts on 40 test datasets compared with expert data and demonstrated an accuracy of 92%. The User Acceptance Test results from 15 respondents yielded an average score of 87.1%. These results indicate that the integration of the Certainty Factor method improves diagnostic precision under uncertain conditions, making this system a practical and accessible tool for early disease detection. This study contributes to the development of intelligent agricultural decision support systems, particularly in the context of digital agriculture and plant health management.