Plant Disease Diagnosis and Detection using Type-2 Fuzzy Logic System

The advancement in technological equipment used in farming practices has stimulated the spread of various diseases in farm management. Most of the time, these infections are capable of crossing international borders and spreading quickly. It has become clear that preventing and identifying these diseases is a big problem that requires urgent attention. Farmers have been plagued by plant disease for many years; a solution must be provided to stop the harm. Hence, there is a need to advance plant disease detection methods since accurate plant disease detection will significantly raise the standard of harvested food crops. and lessen the danger that plant diseases bring to the security of food. Numerous domains, including intelligent control, pattern recognition, and disease classification for both humans and plants, have made extensive use of type-2 fuzzy logic. Therefore, this paper proposes plant disease diagnosis and detection using type-2 fuzzy logic (T2FL). The knowledge for detecting plant diseases is represented by a collection of T2FL rules. A plant disease dataset downloaded from the Kaggle website is used to simulate the T2FL model that is being presented. The modeling of the proposed system was implemented with a separate set of rules. A 97% accuracy rate for the system has been attained, and comparison outcomes demonstrate the credibility of the study.