Application of PSPNET and Fuzzy Logic for Wheat Leaf Rust Disease and its Severity

Correct identification of wheat crop diseases in an accurate manner is helpful for small and large-scale farmers. Once, the wheat diseases are spreading, the whole wheat plant is damaged. In this study, leaf rust disease in the wheat plant along with their severity level system is determined. The main purpose of this paper is to use the Pyramid scene parsing network (PSPNET) and Fuzzy rule models to develop an innovative multilevel model (PSGIC) for estimating wheat leaf rust with their infection level. The study takes 1266 wheat plant images from secondary sources and these images have been preprocessed using image processing techniques. After preprocessing, a total of 315 bad condition images have been removed from the whole dataset. A prediction mask of wheat leaf rust is mapped using PSPNET and severity is estimated with five fuzzy rules. The evaluation of PSPNET is measured between output and predicted mask in terms of Mean square error. By using the proposed PSGIC wheat leaf rust prediction model, an effective decision can be made about wheat rust disease identification.