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基于机器视觉的板栗分级检测方法 被引量:64

Determination of chestnuts grading based on machine vision
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摘要 为实现合格和缺陷板栗的分级,研究了1种基于BP神经网络与板栗图像特征的板栗分级方法。试验以罗田板栗为研究对象,提取的颜色及纹理等8个特征值,通过主成分分析提取相应的主成分得分向量构成模式识别的输入。利用BP神经网络方法建立了板栗分级模型。试验结果表明,在图像信息主成分因子数为3,中间层节点数为12时,建立的模型最佳,模型训练时的回判率为100%,预测时识别率达到了91.67%。研究结果表明基于机器视觉技术的针对缺陷板栗分级检测方法是可行的。 In order to realize grading of eligible and defected chestnut by using machine vision,a classification method of chestnut was developed based on BP-ANN and image feature of chestnut.In this experiment,Luotian chestnuts were used as experimental targets.Principal component analysis(PCA)was implemented on these feature variables from eight eigen values including color parameters and veins characteristics parameters etc.,and principal components(PCs) vectors were extracted as the inputs of pattern recognition.Grading models were built by BP neural network.The test result showed that when the number of principal component factor was three and the number of nodes of hidden layer was twelve,the discriminating rate was as high as 100% in training set,and 91.67% in prediction set.The overall results shows that it is feasible to discriminate chestnut quality with machine vision.
作者 展慧 李小昱 王为 汪成龙 周竹 黄懿 Zhan Hui,Li Xiaoyu,Wang Wei,Wang Chenglong,Zhou Zhu,Huang Yi (College of Engineering,Huazhong Agricultural University,Wuhan 430070,China)
出处 《农业工程学报》 EI CAS CSCD 北大核心 2010年第4期 327-331,共5页 Transactions of the Chinese Society of Agricultural Engineering
基金 高等学校博士学科点专项科研基金(20090146110018)
关键词 农产品 神经网络 图像处理 板栗 机器视觉 agricultural products neural networks image processing chestnut machine vision
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