【摘要】 为了实现航天育种番茄不同品种的快速光谱鉴别,采用主成分分析法对光谱数据进行聚类分析,并将小波变换用于对大量光谱数据的压缩,同时结合神经网络建立了番茄品种鉴别模型。该模型将压缩后的数据作为神经网络的输入,加速了神经网络的训练速度。通过对太空育种突变株M1和M2及其亲本番茄品种的共105个番茄叶片样本建立训练模型,并用每个品种15个样本,共45个番茄叶片的样本进行预测。结果表明,用该方法对航天育种番茄不同品种的鉴别正确率达到97.8%。说明文章提出的方法具有很好的分类和鉴别作用,为航天育种番茄不同品种的快速鉴别提供了一种新方法。更多还原

【Abstract】 In order to quickly analyze varieties of tomato via space mutation breeding with near infrared spectra,firstly,principal component analysis was used to analyze the clustering of tomato leaf samples,and then abundant spectral data were compressed by wavelet transform and the model was built with radial basis function neural network,which offered a quantitative analysis of tomato varieties discrimination. The model regarded the compressed data as the input of neural network input vectors and the training process speeded up. One hundred and five leaf samples of CK,M1 and M2 were selected randomly to build the training model,and forty five samples formed the prediction set. The discrimination rate of 97.8% was achieved by this method. It offered a new approach to the fast discrimination of varieties of tomato via space mutation breeding.更多还原