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.
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