文献类型：期刊文献

英文题名：Hyperspectral imaging coupled with multivariate methods for seed vitality estimation and forecast for Quercus variabilis

作者：Pang, Lei[1]; Wang, Jinghua[1]; Men, Sen[2,3]; Yan, Lei[1]; Xiao, Jiang[1]

第一作者：Pang, Lei

通讯作者：Yan, Lei

机构：[1] School of Technology, Beijing Forestry University, Beijing, 100083, China; [2] College of Robotics, Beijing Union University, Beijing, 100020, China; [3] Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing Union University, Beijing, 100020, China

第一机构：School of Technology, Beijing Forestry University, Beijing, 100083, China

通讯机构：[1]School of Technology, Beijing Forestry University, Beijing, 100083, China

年份：2021

卷号：245

外文期刊名：Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy

收录：EI(收录号：20203809185695);Scopus(收录号：2-s2.0-85090703501)

语种：英文

外文关键词：Cultivation - Spectroscopy - Nearest neighbor search - Forecasting - Genetic algorithms - Nondestructive examination - Reflection - Discriminant analysis - Germination

摘要：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. ? 2020