Forward chaining method on diagnosis of diseases and pests corn crop

Abstract

Integrated pest management should be done to control the explosion of plants pest and diseases due to climate change is uncertain. This paper is a present implementation of the forward chaining method in the diagnosis diseases and pests of corn crop to help farmers/agricultural facilitators in getting knowledge about disease and pest corn crop. Forward chaining method as inference engine is used to get a disease/pest that attacks the corn crop based on symptoms. The forward chaining method works based on the fact that there is to get a conclusion. Fact in this system derived from the symptoms of the selected user is matched with the premise on every rule in the knowledge base. A rule that matches the facts to be executed to be the conclusion in the form of diagnosis. This validation using 36 data test, 32 data showed the same diagnostic results between systems with an expert. So, the percentage accuracy of results of diagnosis using data test of 88%. Finally, it can be concluded that the diagnosis system of diseases and pests corn crop can be used to help farmers/agricultural facilitators to diagnose diseases and pests corn crop.Integrated pest management should be done to control the explosion of plants pest and diseases due to climate change is uncertain. This paper is a present implementation of the forward chaining method in the diagnosis diseases and pests of corn crop to help farmers/agricultural facilitators in getting knowledge about disease and pest corn crop. Forward chaining method as inference engine is used to get a disease/pest that attacks the corn crop based on symptoms. The forward chaining method works based on the fact that there is to get a conclusion. Fact in this system derived from the symptoms of the selected user is matched with the premise on every rule in the knowledge base. A rule that matches the facts to be executed to be the conclusion in the form of diagnosis. This validation using 36 data test, 32 data showed the same diagnostic results between systems with an expert. So, the percentage accuracy of results of diagnosis using data test of 88%. Finally, it can be concluded that the diagnosis system ...