Diagnosis of Onion Disease With Forward Chaining And Backward Chaining Method

Abstract

Plant diseases are the main enemy of farmers. Many farmers fail to harvest or reduce their agricultural yields because they are not able to properly deal with the diseases that attack their crops. One of the plants that are susceptible to disease is the onion plant. To properly handle the disease that attacks the shallot plant, an agricultural expert is needed. While the number of agricultural experts is limited and unable to deal with the problems of a large number of farmers at the same time, so we need a system that has the capabilities of an agricultural expert, which in this system contains the expertise of an agricultural expert regarding diseases, symptoms and diseases treatment of onion plants. In this study, a Web-based expert system was designed and built using rule-based reasoning with forward chaining and backward chaining inference methods which were intended to assist farmers in diagnosing diseases in shallots, and how to handle them. In this study, forward chaining and backward chaining methods will be compared so that the results will be obtained which method is more suitable for diagnosing a disease. From the results of the comparison analysis of the two methods, it was found that the Forward Chaining method was better and more efficient for diagnosing diseases in shallot plants.