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Otsu, FCM, K-means, Gaussian Distribution.

Diseases in coffee plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Now a day’s coffee plant diseases detection has received increasing attention in monitoring large field of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of coffee plant diseases. This paper presents an automatic identification of Ethiopian coffee plant diseases which occurs on the leaf part and also provides suitable segmentation technique regarding the identifications of the three types of Ethiopian coffee diseases. In this paper different classifiers are used to classify such as artificial neural network (ANN), k-Nearest Neighbors (KNN), Naïve and a hybrid of self organizing map (SOM) and Radial basis function (RBF) .We also used five different types of segmentation techniques i.e. Otsu, FCM, K-means, Gaussian distribution and the combinations of K-means and Gaussian distribution. We conduct an experiment for each segmentation technique to find the suitable one. In general, the overall result showed that the combined segmentation technique is better than Otsu, FCM, K-means and Gaussian distribution and the performance of the combined classifiers of RBF (Radial basis function) and SOM (Self organizing map) together with a combination of k-means and Gaussian distribution is 92.10%.