Expert System for Corn Disease Identification Using Case Based Reasoning Method

Abstract

Corn is the second type of food after rice. However, currently, the level of corn productivity is experiencing problems, with pests and corn diseases. The process of controlling these pests and diseases, if not handled as early as possible, will result in crop failure for corn farmers. Identifying the types of pests and diseases that attack corn plants, is carried out by experts in the field of agriculture, but this process requires quite a long time. Therefore, we need a system that can help farmers diagnose diseases in corn plants, so that the control process can be carried out optimally, quickly and on target. This study aims to build an expert system to identify diseases in corn plants by implementing the Case-Based Reasoning (CBR) method. CBR is a reasoning method on a computer that utilizes old cases to solve new cases. The process of identifying the type of disease with the CBR method is carried out by the user inputting the symptoms experienced by the corn plant into the system, then the system calculates the value of similarity between new cases and old cases using the nearest neighbor method. The system is made with 17 diseases and 56 symptoms, each symptom has a weight. Based on the test results, shows that the system can identify the types of diseases in corn plants following the rule of 100% with a similarity accuracy rate of 75.00%.