| 基于BP神经网络的脱绒棉种品种识别 |
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| 引用本文： | 李景彬,陈兵旗,邵鲁浩,田绪顺,坎 杂.基于BP神经网络的脱绒棉种品种识别[J].农业工程学报,2012,28(26):265-269. |
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| 作者姓名： | 李景彬 陈兵旗 邵鲁浩 田绪顺 坎 杂 |
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| 作者单位： | 1. 中国农业大学工学院，北京 100083； 2. 石河子大学机械电气工程学院，石河子 832000;1. 中国农业大学工学院，北京 100083；;2. 石河子大学机械电气工程学院，石河子 832000;2. 石河子大学机械电气工程学院，石河子 832001;2. 石河子大学机械电气工程学院，石河子 832002 |
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| 基金项目： | unded by National Nature Science Foundation of China (No31260290) |
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| 摘 要： | 摘要：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。
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| 关 键 词： | 神经网络，特征参数提取，识别，品种，脱绒棉种 |
| 收稿时间： | 2011/10/21 0:00:00 |
| 修稿时间： | 2012/8/20 0:00:00 |
Variety identification of delinted cottonseeds based on BP neural network |
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| Li Jingbin,Chen Bingqi,Shao Luhao,Tian Xushun and Kan Za.Variety identification of delinted cottonseeds based on BP neural network[J].Transactions of the Chinese Society of Agricultural Engineering,2012,28(26):265-269. |
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| Authors: | Li Jingbin Chen Bingqi Shao Luhao Tian Xushun and Kan Za |
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| Abstract: | 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 parameters 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 3 000, 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. |
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| Keywords: | neural networks characterization identification varieties delinted cottonseeds |
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