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This paper describes the development of an intelligent diagnosis system for selected psoriasis skin disease by using the application of Artificial Neural Network (ANN). Three major types of psoriasis images were analyzed for color feature extraction from RGB model. Images were taken from selected skin at the dermatological clinic which the images are captured using digital camera with controlled environment. The images would be represented by their gaussian differential mean of each color component where these parameters were trained to produce an optimized ANN model for guttate lesion classification. The proposed model was designed by implementing a multi layer feed forward with backpropagation algorithm. The optimized ANN model in this work has two methods which based on their gaussian differential mean of RGB and applying sample sized reduced on each pixel gradation values of each image. Each optimized model are evaluated and validated through analysis of the performance indicators regularly applied in medical research.

This paper presents a characterization of watermelon leaf diseases through the RGB color. The aim of this study is to perform identification of selected critical watermelon leaf diseases in Malaysia namely the Downy Mildew and Anthracnose diseases. Several samples of infected leaves images were put under digital RGB color extraction where the images were captured under standardized and controlled environment. This study involves 200 samples of infected leaves of which the classification of the diseases was carried out using Fuzzy Logic technique. Fuzzy Logic was used to handle the uncertainty and vagueness as it provides a means of translating qualitative and imprecise information into quantitative (linguistic) terms. The results have shown that the percentage of accuracy for both types of disease were more than 67%.

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