Plant disease recognition based on image processing provides quickly and more reliable diagnosis and control of plant diseases. We extract 15 features (four morphology, five GLCM and six color features) were identified from the image of four kinds of plant i.e maize, mango, banana and avocado. We were used and compare ANN and SOM together with RBF (self organizing map and radial basis function) to identify the plant leaf diseases. The experiments were conducted under four scenarios by using feature sets of morphology, texture and color separately, and finally combining the three feature sets. Then, the experiment results were compared the performance of ANN and SOM together with RBF classification over the three scenarios. The total number of data sets is 10380. Out of these, 70% were used for training and the remaining 30% were used for testing. In general, the overall result showed that morphology and color features have more discriminating power than texture features and the recognition performance of SOM is 92.96 and by far better than ANN.
SOM, RBF, ANN, Plant Diseases.