Papaya Disease Detection Using Fuzzy Naïve Bayes Classifier

Abstract

Papaya is one of the tropical fruits that is grown in Indonesia. The weather condition in Indonesia cause it to be attacked by pest and disease. The farmers have difficulty identifying them due to a lack of knowledge and obtaining information from experts. In this study, an expert system was developed to detect papaya disease. Expert knowledge is applied to the system so the farmer can use it to identify the condition without an expert. It is usually represented in the linguistic form, was converted into numbers using fuzzy reasoning, Triangular Fuzzy Number (TFN) membership function. Then the expert knowledge was processed using the Naïve Bayes Classifier to obtain the results of the disease classification. The test was also performed using forward chaining search methods. The accuracy was 88% for FNBC and 90% for forward chaining compared to expert knowledge.