首页> 外文会议>Proceedings of the Fourteenth International conference on mechanization of field experiments >Variety identification of delinted cottonseeds based on BP neural network
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Variety identification of delinted cottonseeds based on BP neural network

机译:基于BP神经网络的脱水棉种子品种鉴定。

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In order to improve the accuracy of variety identification of delinted cottonseeds,this paper proposed a nonlinear identification method based on Back Propagation (BP) neural network and investigated three varieties of delinted cottonseeds,namely Xinluzao 36,Zhongmian 50,and Huiyuan 710.Applying to image processing techniques,the color and shape characteristic pararmeters of delinted cottonseeds were selected,and then through carrying out univariate analysis of such characteristic parameters,9 characteristic parameters that had significant difference were selected and involved in the network training,which improved the training speed.Through training and comparison,it was found that the training error was the smallest when the training target was 0.02 under condition that training times was 3000,and the node number of hidden layer was 12.After testing the test set,it was found that the comprehensive test accuracy rate was 90%,which indicated that the method was feasible to improve the accuracy of variety identification of delinted cottonseeds.The research can provide a reference for variety identification of other granular seeds.%为了提高脱绒棉种品种识别的准确率,提出了基于BP神经网络的非线性识别方法.该文以新路早36、中棉50、惠远710等3个品种为研究对象,基于图像处理技术提取了脱绒棉种的颜色和形状特征参数,后又通过对特征参数进行单因素分析选取了差别较明显的9个特征参数参与网络的训练,提高了训练速度.经训练比较得出,当训练目标为0.02,训练次数为3 000,隐含层的结点数为12时,模型的训练误差最小.经过对测试集进行测试,得出综合测试准确率为90%,证明了该方法是可行的,提高了脱绒棉种的识别准确率.该研究可为其他粒状种子品种识别提供参考.
机译:为了提高棉籽种子品种鉴定的准确性,提出了一种基于BP神经网络的非线性鉴定方法,研究了棉籽种子的新露枣36号,中棉50号和汇源710号三种带斑点的棉籽品种。通过图像处理技术,选择去皮棉籽的颜色和形状特征参数,然后对这些特征参数进行单变量分析,选择9个差异显着的特征参数进行网络训练,提高了训练速度。通过训练和比较,发现在训练次数为3000,隐层节点数为12的条件下,训练目标为0.02时,训练误差最小。综合测试准确率达到90%,表明该方法对提高测试精度是可行的。该研究可为其他粒状种子的品种鉴定提供参考。%为了提高脱绒棉种子品种识别识别的准确率,提出基于BP神经网络的非线性识别方法。该文以新路早36,中棉50,惠远710等3个品种为研究对象,基于图像处理技术提取了脱绒棉种的颜色和形状特征参数,后又通过对特征参数进行单因素分析选取了不同较明显的9个特征参数参与网络的训练,提高了训练速度。经训练比较得出,当训练目标为0.02,训练次数为3 000,隐含层的结点数为12时,模型的训练误差最小。通过对测试集进行测试,获得综合测试准确率达到90%,证明了该方法是可行的,提高了脱绒棉种的识别准确率。该研究可为其他粒状种子品种识别提供参考。

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