A Fuzzification Model for Predicting the Malus Infection Using Abiotic Parameters

Apple plant diseases can cause significant economic losses to the agricultural industry. Traditional disease detection methods are often time-consuming and laborintensive. This paper aims to explore the utilization of fuzzification for the prediction of apple plant diseases. The use of fuzzification provides a powerful framework for automated and accurate disease diagnosis. This paper presents an overview of the methodology applying fuzzy logic for diagnosing the occurrence of disease at early stage. This is based on the crisp values of abiotic factors such as humidity, temperature and soil, gives predicted risk level in apple plant orchard at four levels as: no risk, low risk, moderate risk and high risk corresponding to the membership values 0, 0.3, 0.7 and 1 respectively.