Ethiopian maize diseases recognition and classification using support vector machine

Abstract

Currently, more than 72 maize diseases found in Ethiopia that attacked different part of maize. There are different traditional mechanisms to identify and classify maize leaf diseases by chemical analysis or visual observation. But, the traditional mechanisms have their own drawbacks take more time and require professional staff. Therefore, many researchers have been doing a lot in identifying and classifying the different types of diseases that attack maize using image processing. However, as far as the researcher's knowledge no attempt has been done for Ethiopian maize diseases dataset. In this study an attempt has been made to develop maize leaf diseases recognition and classification using both support vector machine model and image processing. To evaluate the recognition and classification accuracy from the total dataset of 800 images, 80% used for training and the remaining 20% for testing the model. Based on the experiment result using combined (texture, colour and morphology) features with support vector machine an average accuracy of 95.63% achieved.