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基于等效椭圆和BP神经网络的马铃薯形状分类研究
引用本文:王泽京,高晓阳,毕阳,张明艳,李红岭. 基于等效椭圆和BP神经网络的马铃薯形状分类研究[J]. 甘肃农业大学学报, 2011, 46(3): 131-135
作者姓名:王泽京  高晓阳  毕阳  张明艳  李红岭
作者单位:1. 甘肃农业大学工学院,甘肃兰州,730070
2. 甘肃农业大学食品科学与工程学院,甘肃兰州,730070
摘    要:根据国家马铃薯分级标准的要求,提出了一种基于区域的等效椭圆和BP神经网络相结合的马铃薯形状分类方法.首先运用等效椭圆来提取一组特征参数R和C,然后将这些特征参数输入到已训练好的BP神经网络完成对马铃薯的形状分类.结果表明:该方法选用的特征参数少,能较为有效的描述马铃薯的形状,分级结果准确率达94.7%,与人工分级的一致...

关 键 词:马铃薯  形状分类  等效椭圆  神经网络  特征参数

Potato shape classification based on equivalent ellipse and BP neural network
WANG Ze-jing,GAO Xiao-yang,BI Yang,ZHANG Ming-yan,LI Hong-ling. Potato shape classification based on equivalent ellipse and BP neural network[J]. Journal of Gansu Agricultural University, 2011, 46(3): 131-135
Authors:WANG Ze-jing  GAO Xiao-yang  BI Yang  ZHANG Ming-yan  LI Hong-ling
Abstract:The shape is one of the important features for potato integrated classification.According to the requirements of classification,a potato shape classification method combining region-based equivalent ellipse with BP neural network was presented.First,shape features were extracted from region-based equivalent ellipse,and then these features were input to the trained BP neural network to be completed the potato shape classification.The experiments showed that this method could describe the shape feature of the potato effectively by using the less feature parameters.The grading results were consistent with those of artificial classification highly,its precision was up to 94.7% and could meet the requirements of practical application.
Keywords:potato  shape classification  equivalent ellipse  neural network  characteristic parameter
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