Identifying Types of Peanuts Diseases with Naive Bayes Method - Buntoro, Ghulam Asrofi; Fahmi, Anas; Astuti, Indah Puji (Intelligent System and Computation, 2025)
Peanut plants are susceptible to various constraints that significantly hinder their productivity, with eight prevalent diseases posing serious threats to their health. The peanut plant is one of the important commodities in Indonesia; peanuts play a strategic role in supporting the country's economy and food, where peanuts are a source of protein and a source of vegetable oil. Many farmers, especially those new to peanut cultivation, often lack the necessary knowledge to identify and manage these diseases effectively. To address this gap, this study developed an expert system that employs the Naive Bayes method to facilitate the identification of peanut plant diseases. This system aims to provide farmers with accessible and accurate information regarding symptoms, disease types, and management strategies. The knowledge base for the expert system was constructed from data gathered from peanut farming experts, ensuring the reliability of the information provided. Testing of the system revealed consistent results with manual calculations, particularly in identifying Sclerotium stem rot disease with a probability value of 0.44507. Additionally, the system successfully recognized leaf rust disease during its evaluation. By equipping farmers with a user-friendly tool for disease identification and management, this expert system seeks to enhance their understanding and response to peanut plant diseases, ultimately improving productivity and sustainability in peanut farming. The findings underscore the potential of integrating technology into agriculture to support farmers in overcoming challenges related to crop health.