title: [Study on method of maize hybrid purity identification based on hyperspectral image technology].
abstract: The feasibility of employing hyperspectral image technology to identify maize hybrid purity was studied by analyzing the spectral information of maize hyperspectral image. The hyperspectral images of hybrid and female parent of maize variety NH101 in the range of 871-1 699 nm including 308 wavelengths were collected by hyperspectral imaging system. We extracted average spectral information of interested region on maize seed and built identification models of hybrid and female parent of maize variety NH101 based on processed spectral data. The influences of different sample laying modes (seed embryo facing the light source, seed embryo backward light source, and seed put in different locations on sample stage) and experimental environments on the performance of identification models were discussed. Spectral collected under different sample laying modes and experimental environments were used to test the robustness of identification models. The average correct acceptance rates and average correct rejection rates are more than 90%. The feature spectral bands (1 195-1 246 nm) with which the differences between hybrid and female parent are the largest were extracted by a wavelength selection method based on standard deviations, called Qs. The performance of identification models built based on spectral data in feature spectral bands reached the same level of models built based on spectral data in the full range of 925-1 597 nm. The results demonstrated the feasibility of using hyperspectral image technology as an objective and rapid method for the identification of maize hybrid purity.
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China. jiashiqiang@cau.edu.cn
authors.forename: Shi-Qiang
authors.initials: SQ
authors.lastname: Jia
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Liu
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: Shao-Ming
authors.initials: SM
authors.lastname: Li
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Li
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Ma
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: Xiao-Dong
authors.initials: XD
authors.lastname: Zhang
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: De-Hai
authors.initials: DH
authors.lastname: Zhu
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: Yan-Lu
authors.initials: YL
authors.lastname: Yan
authors.affiliation: College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
authors.forename: Dong
authors.initials: D
authors.lastname: An
mesh_terms: D006824:Hybridization, Genetic; D008027:Light; D008962:Models, Theoretical; D012639:Seeds; D013057:Spectrum Analysis; D003313:Zea mays / Q000145:classification*