用神经网络方法进行大米留胚率自动检测的研究

留胚率是衡量胚芽米品质的主要技术指标。该文建立了一个双重结构神经网络分类器，用机器视觉获取胚芽米图像，从中提取米粒的物理特性作为网络分类器的输入进行训练，实现了留胚率的自动检测。测试结果表明该方法准确率较高并具有鲁棒性。

Research on Detecting the Plumule Ratio of Rice Kernel Using a Neural Network Approach

Plumule ratio is of the most important criterion for evaluating the quality of plumule rice. A dual structure neural network classifier was developed which consisted of two parallel identifiers(one per type)followed by a comparing selector. Images of rice kernels were captured using a machine vision system. The identifiers were individually trained using physical attributes of rice kernel extracted from their images as the input. Then the classifier can be used to classify two types of rice kernel(kernel with or without plumule).And the plumule ratio of rice can be measured automatically. Tests showed that classification accuracy was high and the classifier was robust.