Next Article in Journal
Assigning Main Orientation to an EOH Descriptor on Multispectral Images
Next Article in Special Issue
Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera
Previous Article in Journal
A Sensitive and Selective Label-Free Electrochemical DNA Biosensor for the Detection of Specific Dengue Virus Serotype 3 Sequences
Previous Article in Special Issue
VIS-NIR, SWIR and LWIR Imagery for Estimation of Ground Bearing Capacity
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(7), 15578-15594; doi:10.3390/s150715578

Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification

College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 5 April 2015 / Revised: 25 June 2015 / Accepted: 26 June 2015 / Published: 1 July 2015
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
View Full-Text   |   Download PDF [3063 KB, uploaded 1 July 2015]   |  

Abstract

The purity of waxy corn seed is a very important index of seed quality. A novel procedure for the classification of corn seed varieties was developed based on the combined spectral, morphological, and texture features extracted from visible and near-infrared (VIS/NIR) hyperspectral images. For the purpose of exploration and comparison, images of both sides of corn kernels (150 kernels of each variety) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing and derivation. To reduce the dimension of spectral data, the spectral feature vectors were constructed using the successive projections algorithm (SPA). Five morphological features (area, circularity, aspect ratio, roundness, and solidity) and eight texture features (energy, contrast, correlation, entropy, and their standard deviations) were extracted as appearance character from every corn kernel. Support vector machines (SVM) and a partial least squares–discriminant analysis (PLS-DA) model were employed to build the classification models for seed varieties classification based on different groups of features. The results demonstrate that combining spectral and appearance characteristic could obtain better classification results. The recognition accuracy achieved in the SVM model (98.2% and 96.3% for germ side and endosperm side, respectively) was more satisfactory than in the PLS-DA model. This procedure has the potential for use as a new method for seed purity testing. View Full-Text
Keywords: waxy corn; hyperspectral imaging; SPA; SVM; PLS-DA; variety classification waxy corn; hyperspectral imaging; SPA; SVM; PLS-DA; variety classification
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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yang, X.; Hong, H.; You, Z.; Cheng, F. Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification. Sensors 2015, 15, 15578-15594.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

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