基于神经网络与图像处理的花生仁霉变识别方法

为了采用机器视觉对霉变花生仁的自动识别与分选，研究了一种基于花生仁图像特征和人工神经网络的霉变识别方法。首先，利用Sobel算子直接对噪声含量少、边缘保存较完整的B分量灰度图进行边缘检测，经过形态学滤波、填充、合成等处理去除背景，得到分割后的彩色花生仁图像。然后提取颜色特征H、I、S及纹理特征RW、GW、BW，将其作为MATLAB所创建的神经网络的输入，并分别定义正常、轻度霉变、严重霉变3组代码为100、010、001的类型作为网络的输出，建立特征参数与霉变等级之间的神经网络识别模式。试验结果表明，该方法对正常花生仁、轻度霉变花生仁、严重霉变花生仁的检测准确率分别为95%、90%、100%，得到了较好的识别效果。

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.