|Table of Contents|

Variety Recognition of Soybeans Using Segmented Principal Component Analysisand Hyperspectral Technology(PDF)

《大豆科学》[ISSN:1000-9841/CN:23-1227/S]

Issue:
2016年04期
Page:
672-678
Research Field:
Publishing date:

Info

Title:
Variety Recognition of Soybeans Using Segmented Principal Component Analysisand Hyperspectral Technology
Author(s):
LIU Yao12TAN Ke-zhu1CHEN Yue-hua1WANG Zhi-peng1XIE Hong2WANG Li-guo2
1. College of Electric and Information,Northeast Agricultural University,Harbin 150030,China; 2. College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China
Keywords:
Hyperspectral Segmented principal component analysis Soybean Varity identification
PACS:
TP391. 41; TP274. 5
DOI:
10.11861/j.issn.1000-9841.2016.04.0672
Abstract:
In order to realize rapid nondestructive recognition for soybeans varieties, spectral information of hyperspectral imagefor soybeans is analyzed. Hyperspectral images for 660 soybeans including 6 varieties from 400 to 1 000 nm are acquired byhyperspectral image system. The interested region for each sample is extracted and average spectral information is obtained.Spectrum curve is conducted multiple scattering correction ( MSC) . According to the correlation coefficient matrix,the entirehyperspectral bands are segmented into three highly relevant sub-segments. Principal component analysis ( PCA) is respectivelyused in each sub-segments. The first twenty principal components are extracted as spectral features of soybean samples.Identification model is developed using Extreme Learning Machine ( ELM) and Random Forests ( RF) models. Experimentalresults indicate that the identification results using PCA in the second sub-segment ( 510. 6 - 685. 4 nm) is outperform than usingPCA in the entire bands. Nondestructive recognition and classification for soybean varieties using segmented PCA and hyperspectraltechnology is effective.

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Last Update: 2016-08-23