Zhu Qi-Bing, Feng Zhao-Li, Huang Min, Zhu Xiao
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2013 Feb;33(2):517-21.
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.
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准确率的两个关键问题。在本研究中，利用高光谱图像系统采集玉米种子的高光谱图像，并提取每个波长下玉米种子的图像熵作为分类特征。然后，使用支持向量数据描述（SVDD）算法为每种玉米种子建立分类器模型。SVDD模型对已知品种样本的平均测试准确率分别为94.14%，对新品种样本的平均测试准确率为92.28%。仿真结果表明，该方法实现了玉米种子的准确识别，解决了传统分类算法对新品种玉米种子误分类的问题。
