Abstract:
Abstract: The diagnosis of diseases on the plant is a very important to provide large quantity and good qualitative
agricultural products. Enset is an important food crops produced in Southern parts of the Ethiopia with great role in food
security. There are several issues and diseases which try to decline the yield with quality. Particularly, diagnosis of potential
diseases on Enset is based on traditional ways. The aim of this study is to design a model for Enset diseases diagnosis using
Image processing and Multiclass SVM techniques. This study presented a general process model to classify a given Enset leaf
image as normal or infected. The strategy of K-fold stratified cross validation was used to enhance generalization of the model.
This diagnosis apply K-means clustering, color distribution, shape measurements, Gabor texture extraction and wavelet
transform as key approaches for image processing techniques. The researcher selected two Enset leaf diseases viz. Bacterial
Wilt and Fusarium Wilt disease and collected 430 Enset leaf images from Areka agricultural research center and some selected
areas in SNNPR. For this research work MATLAB version R2017a tool was used as a platform to simulate the real world data.
The proposed model demonstrated with four different kernels, and the overall result indicates that the RBF Kernel achieves the
highest accuracy as 94.04% and 92.44% for bacterial wilt and fusarium wilt respectively. Therefore, an efficient practice of IT
based solution in this domain will increases productivity and quality of Enset products.
