特征提取的充分性和分类器设计的合理性是影响玉米种子识别精度的两个关键问题。 采集了玉米种子的高光谱图像, 并提取每粒玉米种子在不同波段下的图像熵作为分类特征; 在此基础上, 利用支持向量数据描述方法构建每类玉米的分类器模型, 对待识别样本的测试精度达到了94.14%, 对新类别样本的识别精度达到92.28%。 仿真结果表明: 新方法可实现玉米种子的准确识别, 同时解决了传统分类器对新类别样本的错误分类问题。

The sufficiency of feature extraction and the rationality of classifier design are two key issues affecting the accuracy of maize seed recognition. In the present study, the hyperspectral images of maize seeds were acquired using hyperspectral image system, and the image entropy of maize seeds for each wavelength was extracted as classification features. Then, support vector data description (SVDD) algorithm was used to develop the classifier model for each variety of maize seeds. The SVDD models yielded 94.14% average test accuracy for known variety samples and 92.28% average test accuracy for new variety samples, respectively. The simulation results showed that the proposed method implemented accurate identification of maize seeds and solved the problem of misclassification by the traditional classification algorithm for new variety maize seeds.