Web-Based Expert System for Dragon Fruit Disease Diagnosis Using Bayes Method

Dragon fruit cultivation in Kampung Daun Baumata has faced significant challenges due to plant diseases, with farmers reporting a 15% yield reduction in 2022. This study addresses this critical agricultural problem by developing an innovative web-based expert system that utilizes Bayesian probability theory for accurate and timely disease diagnosis. The system provides farmers and agricultural stakeholders with an accessible digital tool to identify common dragon fruit diseases, including stem rot, anthracnose, and fungal infections, through symptom analysis and probability calculations. Implemented using PHP programming language and MySQL database, the expert system offers several advantages over traditional diagnostic methods. It operates independently of human experts, delivers real-time results, and provides prevention recommendations. The Bayesian approach enables the system to process uncertain information and update disease probabilities as new symptom data becomes available, significantly improving diagnostic accuracy compared to conventional methods. Field testing demonstrates the system's effectiveness in supporting farmers' decision-making processes, enabling early disease detection, and facilitating appropriate treatment measures. The implementation of this technological solution has the potential to reduce economic losses, improve crop yields, and promote sustainable farming practices in dragon fruit cultivation. By bridging the gap between farmers and agricultural expertise, this research contributes to the digital transformation of agricultural disease management in developing regions.