Modeling for mung bean variety classification using visible and near-infrared hyperspectral imaging

作     者：Chuanqi Xie Yong He 

作者机构：College of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhou 310058China 

出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)

年 卷 期：2018年第11卷第1期

页      面：187-191页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Key Scientific Instrument and Equipment Development Projects(2014YQ470377) the Scientific Research Foundation for Returned Overseas Students and the Fundamental Research Funds for the Central Universities of China(2012FZA6005,2013QNA6011) 

主　　题：visible and near-infrared hyperspectral imaging mung bean classification modeling wavelength selection 

摘      要：This study was carried out to investigate the feasibility of using visible and near infrared hyperspectral imaging for the variety classification of mung *** hyperspectral images of mung beans were acquired in the wavelengths of 380-1023 nm,and all images were calibrated by the white and dark reference *** spectral reflectance values were extracted from the region of interest(ROI)of each calibrated hyperspectral image,and then they were treated as the independent *** dependent variables of four varieties of mung beans were set as 1,2,3 and 4,*** extreme learning machine(ELM)model was established using full spectral wavelengths for *** gram-schmidt(MGS)method was used to identify effective *** on the selected wavelengths,the ELM and linear discriminant analysis(LDA)models were *** models performed excellently with the correct classification rates(CCRs)covering 99.17%-99.58% in the training sets and 99.17%-100%in the testing *** wavelengths(432 nm,455 nm,468 nm,560 nm,705 nm,736 nm,760 nm,841 nm,861 nm,921 nm,930 nm,937 nm,938 nm,959 nm and 965 nm)were recommended by *** results demonstrated that hyperspectral imaging could be used as a non-destructive method to classify mung bean varieties,and MGS was an effective wavelength selection method.