Expert System for Identifying Pests and Diseases in Maize Plants Using Web-Based Bayes Theorem Method

Abstract

Corn is one of the world's most popular carbohydrate-producing food crops, besides wheat and rice. The large number of corn plants attacked by pests and diseases can disrupt corn productivity and the community's economy because corn plants can be damaged, resulting in lower prices and quality of crops. This system aims to build an expert system to identify eight types of diseases and nine pests in corn plants from 49 symptoms using the Bayes Theorem method and make it easier for corn farmers to carry out control after knowing that there are identified pests and diseases. The Bayes Theorem method is a method for dealing with data uncertainty. This method is based on the initial conditions, which are the conditions of the existing symptoms, then subject to predetermined rules. Then the largest truth value is taken to determine conclusions and solutions to the previously mentioned symptoms. The results of 100% functionality have been successfully tested through black box testing. The results of the evaluation of the accuracy of the Bayes Theorem Method for identifying pests on corn plants amounted to 94.23%.