针对小麦品种多分类问题, 使用近红外光谱进行定性分析。 建模样本增加能够使模型包含信息增多, 但同时也会导致信息冗余, 增加建模时间和存储空间, 所以需要通过样本选择降低数据量。 如果盲目选择必然会使信息丢失, 模型效果将大打折扣, 因此, 在传统选择方法基础上, 提出k近邻-密度样本选择方法。 使用多天采集的小麦种子近红外漫反射光谱, 在对其原始光谱进行预处理和特征提取后, 分别使用随机抽样、 k近邻和k近邻-密度三种方法进行建模样本选择, 然后建立仿生模式识别模型和改进的仿生模式识别模型。 实验结果显示, 在建立的仿生模式识别模型中, 使用k近邻-密度样本选择方法的模型识别效果优于另两种方法, 且建模样本量大大降低； 而在改进的仿生模式识别模型中, 使用k近邻-密度样本选择方法识别效果明显优于随机抽样, 略好于k近邻方法, 但使用k近邻-密度方法所选择的样本数量远少于k近邻方法。 结果证明k近邻-密度样本选择方法不仅能够大大降低建模样本量, 而且保证了模型质量, 对解决小麦品种多分类问题有明显效果。

For more wheat varieties classification problem, we use near infrared spectrumto do qualitative analysis. Increasing the size of modeling sample could increase information of the model, however, at the same time, it also makes information redundancy so that modeling time and storage space will increase, thus, we need to decrease the size of modeling sample though selecting them. Some information must be lost and the effects of the model must be worse if we select samples blindly. We put forward the k nearest neighbor-density sample selection based on the traditional selection methods. Experiments use the near infrared diffuse reflection spectrum of wheat seed from lots of days. First, we use preprocessing and feature extraction to deal with the wheat original spectrum, then select modeling sample by three methods that are random sampling, k nearest neighbor and k nearest neighbor-density, finally, we establish the models of BPR(Biomimetic Pattern Recognition) and BPRI(Biomimetic Pattern Recognition Improved). The experimental results show that in the model of BPR we get the best results using the selection method of k nearest neighbor-density, especially it also decreases the size of modeling sample deeply, and in the model of BPRI the results using the selection method of k nearest neighbor-density are much better than random sampling and a little better than k nearest neighbor, but in the meanwhile the size of modeling sample using the selection method of k nearest neighbor-density are much smaller than k nearest neighbor. The experimental results prove that the sample selection method of k nearest neighbor-density can not only greatly reduce the modeling sample size, and ensure the quality of the model, it has obvious effect on varieties classification problem of wheat.