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Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis.
Pang, Lei; Wang, Jinghua; Men, Sen; Yan, Lei; Xiao, Jiang.
Afiliação
  • Pang L; School of Technology, Beijing Forestry University, Beijing 100083, China.
  • Wang J; School of Technology, Beijing Forestry University, Beijing 100083, China.
  • Men S; College of Robotics, Beijing Union University, Beijing 100020, China; Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing Union University, Beijing 100020, China.
  • Yan L; School of Technology, Beijing Forestry University, Beijing 100083, China. Electronic address: mark_yanlei@bjfu.edu.cn.
  • Xiao J; School of Technology, Beijing Forestry University, Beijing 100083, China.
Spectrochim Acta A Mol Biomol Spectrosc ; 245: 118888, 2021 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-32947159
ABSTRACT
In this study, the feasibility of estimation and forecast of different vitality Quercus variabilis seeds by a hyperspectral imaging technique were investigated. Artificially accelerated aging was conducive to achieve the division of four vitality levels. Hyperspectral data in the first 10 h of germination were continuously collected at one-hour intervals. The optimal band was selected for the original and pre-processed spectra which were treated by multiple scatter correction (MSC) and the Savitzky-Golay first derivative (SG 1st). Five characteristic wavelength methods were compared successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), genetic algorithm (GA), variable important in projection (VIP), and random frog (RF). Partial least square-discriminant analysis (PLS-DA) and K-nearest neighbor (KNN) built the vitality estimation model based on different data sets, and GA + PLS-DA constructed the optimal model with the highest accuracy. According to the weight coefficient and reflectance of the characteristic band extracted by the GA, the reflectance curves of different levels over time were plotted. The data of 0 h was employed to establish the vitality forecast model. The forecast model had a high recognition rate, with PLS-DA exceeding 99% and KNN exceeding 85%. This indicated that hyperspectral imaging of seed germination processes could achieve non-destructive estimation of Q. variabilis seed vitality, and accurate prediction in a shorter time is feasible.
Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Tipo de estudo: Estudo prognóstico Idioma: Inglês Revista: Spectrochim Acta A Mol Biomol Spectrosc Assunto da revista: Biologia Molecular Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: China

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Tipo de estudo: Estudo prognóstico Idioma: Inglês Revista: Spectrochim Acta A Mol Biomol Spectrosc Assunto da revista: Biologia Molecular Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: China