关键词: 小麦籽粒, 特征选择, 品种识别, 特征融合, 数据降维

Abstract: In order to effectively classify wheat grain varieties and identify the characteristics that affect the identification of wheat grain varieties,  this paper research the identification of wheat grain varieties based on feature selection. First,  18 000 images of wheat grains from six varieties of Nongda 3416-18,  Neile 288,  Hengshui 6632,  Bainong 419,  Luomai 28,  and Xinmai 26 were collected. After preprocessing the collected images,  a total of 28 feature values were extracted in three categories of color,  morphology,  and texture features of wheat grains,  and the correlation analysis of features was carried out. Then,  different feature fusion models,  feature data dimensionality reduction models,  and data enhancement models were constructed. Finally,  the experimental analysis showed that the average accuracy of the threefeature fusion model based on texture + shape + color was 91.02%. Luomai 28,  based on the texture + shape + color feature model,  was the highest,  reaching 97.0%. After dimension reduction by Linear Discriminant Analysis (LDA),  the recognition accuracy of wheat feature data recognition was 86.19%,  and the training time of the model was only 0.87 s. After data enhancement,  the average recognition accuracy reached 94.26%. The experiments showed that the wheat grain recognition based on feature selection was feasible,  which was helpful and practical for breeders to make more accurate judgments on the identification of wheat grains.

Key words: wheat grain, feature selection;variety recognition;feature fusion, data dimensionality reduction