Next Article in Journal
Dental Optical Coherence Tomography
Next Article in Special Issue
An Approach for Characterizing and Comparing Hyperspectral Microscopy Systems
Previous Article in Journal
Background Subtraction Based on Color and Depth Using Active Sensors
Previous Article in Special Issue
Estimation of Melanin and Hemoglobin Using Spectral Reflectance Images Reconstructed from a Digital RGB Image by the Wiener Estimation Method
Sensors 2013, 13(7), 8916-8927; doi:10.3390/s130708916
Article

Rice Seed Cultivar Identification Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis

1
,
1
,
1
,
1,*  and 1,2,*
Received: 21 May 2013 / Revised: 26 June 2013 / Accepted: 4 July 2013 / Published: 12 July 2013
(This article belongs to the Special Issue Spectral Imaging at the Microscale and Beyond)
View Full-Text   |   Download PDF [530 KB, 21 June 2014; original version 21 June 2014]   |   Browse Figures

Abstract

A near-infrared (NIR) hyperspectral imaging system was developed in this study. NIR hyperspectral imaging combined with multivariate data analysis was applied to identify rice seed cultivars. Spectral data was exacted from hyperspectral images. Along with Partial Least Squares Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), K-Nearest Neighbor Algorithm (KNN) and Support Vector Machine (SVM), a novel machine learning algorithm called Random Forest (RF) was applied in this study. Spectra from 1,039 nm to 1,612 nm were used as full spectra to build classification models. PLS-DA and KNN models obtained over 80% classification accuracy, and SIMCA, SVM and RF models obtained 100% classification accuracy in both the calibration and prediction set. Twelve optimal wavelengths were selected by weighted regression coefficients of the PLS-DA model. Based on optimal wavelengths, PLS-DA, KNN, SVM and RF models were built. All optimal wavelengths-based models (except PLS-DA) produced classification rates over 80%. The performances of full spectra-based models were better than optimal wavelengths-based models. The overall results indicated that hyperspectral imaging could be used for rice seed cultivar identification, and RF is an effective classification technique.
Keywords: rice seed cultivar; hyperspectral imaging; random forest (RF); weighted regression coefficients (BW) rice seed cultivar; hyperspectral imaging; random forest (RF); weighted regression coefficients (BW)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Kong, W.; Zhang, C.; Liu, F.; Nie, P.; He, Y. Rice Seed Cultivar Identification Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis. Sensors 2013, 13, 8916-8927.

View more citation formats

Related Articles

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert