Image processing system for identifying groundnut plant disease

Abstract

Groundnuts (Arachis hypogaea), also known as peanuts are the edible seeds of a legume plant that grow to maturity in the ground. Diseases and its diagnosis methodology are the main challenges to groundnut production. Manual means of identifying plant disease is the most difficult activity with a high rate of mistake and time taking procedures. Therefore, the main aim of this research was to design an image processing system to identify groundnut plant disease. In general, the major significance of the study is to provide effective and simple groundnut plant disease diagnosis system that supports disease controlling mechanism and experts in the domain area. While the process of developing the system, researchers used purposive sampling techniques for acquiring 320 sample leaves images from four classes of groundnuts plant. Those are Cercospora personatum, Cercospora arachidicola, Puccinia arachidis, and healthy leaf from Bishan-Babile and Gemechu peasant associations in Babile district, and Awdal peasant association in Gursum districts in East Hararghe zone of Oromiya Regional State. Also, the researchers conducted a survey study which helps to identify experts view and deep understanding of the domain area. The design, phase used; adaptive median filtering, K-means clustering, SVM and ANN techniques with MATLAB programming tool. In general, the overall result shows that the developed system achieved a better result with an accuracy of 90.6%.