遗传神经网络在稻米垩白度检测中的应用研究  

Inspection of chalk degree of rice using genetic neural network

作　　者：黄星奕[1] 吴守一[1] 方如明[1] 罗玉坤[2] 

机构地区：[1]江苏大学生物与环境工程学院,江苏镇江212013 [2]中国水稻研究所,杭州310006

出　　处：《农业工程学报》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

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.

关 键 词：稻米 垩白度 人工神经网络 遗传算法 品质检测 

分 类 号：TP389.1[自动化与计算机技术—计算机系统结构] S37[自动化与计算机技术—计算机科学与技术]