Identification method for moldy peanut kernels based on neural network and image processing

To improve the accuracy of the automatic detection and classification of the moldy peanut kernels by using the machine vision, a discriminating method was developed based on image features of the peanut kernels and artificial neural network. First, a segmented colorful image of peanut kernel was obtained by edges detecting, filtering, filling and composing and so on, to remove the background to those relatively complete edge-preserved B branch grey image with less noise. The image characteristics parameters such as the color parameters H, I, S, and veins characteristics parameters RW, GW, BW were used as the input to the neural network set up by MATLAB. Three outputs were defined as 100, 010, 001 which represented the normal level, slightly moldy level and severely moldy level, and an identifying model of the neural network was set up between the feature parameters and moldy grade of the peanut. The results of the experiment show that the accuracies of the identification of the method are 95% for normal peanut kernels, 90% for slightly moldy peanut kernals and 100% for severely moldy peanut kernels, respectively.