<![CDATA[Corn (Zea mays L.) is one of the agricultural commodities that has economic value and is widely cultivated in Indonesia. Corn productivity can decrease due to various leaf diseases, such as downy mildew, common rust, and leaf blight. Similar symptoms among these diseases make it difficult for farmers, particularly Bonanza F1 corn farmers, to accurately identify the disease and determine the appropriate treatment. The problem faced by the Food Security and Agriculture Office of Binjai City is the limited knowledge of farmers and limited access to agricultural experts in diagnosing diseases quickly and accurately. This study aims to develop an expert system to identify leaf diseases in corn plants based on the symptoms observed using the fuzzy logic sugeno method. The method involves fuzzification, inference using if–then rules, and defuzzification to produce disease diagnoses and severity levels. The test results showed that the system produced a diagnosis of Common Rust (P01) with a defuzzification value of 25.00, a confidence percentage of 33.33%, and a low severity category. Therefore, the fuzzy logic sugeno method can assist in identifying leaf diseases in corn plants more systematically and provide diagnostic information and appropriate treatment recommendations based on the available knowledge base.]]>
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