基于遗传算法与线性鉴别的近红外光谱玉米品种鉴别研究

结合遗传算法与线性签别分析(LDA)提出了一种玉米品种的快速鉴别方法.该方法是一种基于近红外光谱的新方法,通过采集玉米种子(实验共37个种类)的近红外光谱数据,使用遗传算法进行特征光谱波段的选择,使用线性鉴别分析的方法提取光谱特征并分类.结果表明,遗传算法能有效地剔除光谱噪声波段,并提高 LDA 的泛化能力.同时,为简化运算,剔除了大量冗余数据,结合遗传算法选择的特征谱区,使参与鉴别的数据维数从2 075降到了233.对测试集1的300个样本的平均正确识别率与平均正确拒识率均达到99.30%,其中73.33%的玉米品种的正确识别率达到了100%;对测试集2(均为未参加训练品种的样本)的175个样本的平均正确拒识率达到99.65%.与常用的 PCA 等方法相比,运算时间更短,正确率更高.

Study on discrimination of varieties of corn using near-infrared spectroscopy based on GA and LDA

A new method for the fast discrimination of varieties of corn based on near-infrared spectroscopy using genetic algorithm and linear discriminant analysis (LDA) was proposed. First, data of NIS of 37 varieties of corn was collected, second, genetic algorithm used for choosing the feature band of spectrum, then PCA and LDA were used to extract features, and finally corn seeds were classified. The result showed that GA could remove noise band effectively and improve the generalization ability of LDA. A large number of redundant data was removed to simplify the computing, which resulted in the data dimension reduction from 2075 to 233. For the 300 samples of test set one, the average correct recognition rate and average correct rejection rate attained 99.30% for both, and the average correct recognition rate of 73.33% varieties of corn attained for 100%. For the 175 samples of test set 2 (all of whose varieties had not been trained), the average correct recognition rate attained 99.65%. The run time is shorter and the correct rate is higher compared to the common method of PCA.