摘要
新的优质稻谷国家标准中 ,垩白度是 4个定级指标之一 ,被用来代表稻谷的商品品质。垩白度的检测目前仍由人工目测完成。为使检测结果更具客观性、一致性 ,建立了遗传神经网络对垩白像素和胚乳其它像素进行了识别 ,从而实现了垩白度的自动无损检测。对两种市售粳米进行了检测 ,计算机视觉的检测结果与人工检测结果的误差小于 0 .0 5。试验结果表明所建立的新方法是可行的 ,它为开发垩白度在线检测系统提供了科学依据。
Chalk degree is one of the four important criteria for judgment of rice quality according to China National Standard of Rice. It has been determined by human inspection exclusively so far. A new method was developed to identify chalk and to grade chalk degree of rice using genetic algorithm and neural network in conjunction with computer vision. Genetic neural network was trained to identify chalk pixels and other pixels of endosperm and subsequently to evaluate chalk degree of rice. Two different kinds of rice bought on market were tested to evaluate system performance. Compared experiment results of new method using genetic neural network with that of human inspection, the error rate was less than 0.05 . This method is proved to be robust and consistent. It paves the way for on line automated judgment of chalk degree of rice.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2003年第3期137-139,共3页
Transactions of the Chinese Society of Agricultural Engineering
基金
教育部留学回国人员启动基金项目
关键词
稻米
垩白度
人工神经网络
遗传算法
品质检测
rice
chalk degree
artificial neural network
genetic algorithm
quality inspection