title: [Maize seed identification using hyperspectral imaging and SVDD algorithm].
abstract: 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.
authors.affiliation: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China. zhuqib@163.com
authors.forename: Qi-Bing
authors.initials: QB
authors.lastname: Zhu
authors.forename: Zhao-Li
authors.initials: ZL
authors.lastname: Feng
authors.forename: Min
authors.initials: M
authors.lastname: Huang
authors.forename: Xiao
authors.initials: X
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted; D012639:Seeds / Q000737:chemistry*; D013057:Spectrum Analysis / Q000379:methods; D060388:Support Vector Machine; D003313:Zea mays / Q000737:chemistry*