<![CDATA[This research introduces an expert system using the Naive Bayes method to diagnose corn plant diseases, aiming to provide an automated, accurate, and scalable diagnostic tool. Traditional methods are often inefficient and error-prone, relying on expert knowledge and manual inspection. This study employs a quantitative approach, incorporating experimental design, data analysis, and model validation. Data on humidity, temperature, and soil conditions were collected from agricultural research centers and online databases. After preprocessing, key variables influencing disease occurrence were selected. The Naive Bayes model was optimized using cross-validation and implemented in Python, achieving an average accuracy of 92%. The model's performance, evaluated through accuracy, precision, recall, and F1-score, demonstrated the effective distinction between similar symptoms—the system's simplicity and computational efficiency suit resource-constrained environments like rural farms. By combining visual symptoms and environmental factors, the system minimizes dependency on expert knowledge, offering a comprehensive and scalable solution for disease management in agriculture]]>
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