Cargando…
Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification
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 p...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541845/ https://www.ncbi.nlm.nih.gov/pubmed/26140347 http://dx.doi.org/10.3390/s150715578 |
_version_ | 1782386446143848448 |
---|---|
author | Yang, Xiaoling Hong, Hanmei You, Zhaohong Cheng, Fang |
author_facet | Yang, Xiaoling Hong, Hanmei You, Zhaohong Cheng, Fang |
author_sort | Yang, Xiaoling |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-4541845 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-45418452015-08-26 Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification Yang, Xiaoling Hong, Hanmei You, Zhaohong Cheng, Fang Sensors (Basel) Article 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. MDPI 2015-07-01 /pmc/articles/PMC4541845/ /pubmed/26140347 http://dx.doi.org/10.3390/s150715578 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Yang, Xiaoling Hong, Hanmei You, Zhaohong Cheng, Fang Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification |
title | Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification |
title_full | Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification |
title_fullStr | Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification |
title_full_unstemmed | Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification |
title_short | Spectral and Image Integrated Analysis of Hyperspectral Data for Waxy Corn Seed Variety Classification |
title_sort | spectral and image integrated analysis of hyperspectral data for waxy corn seed variety classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4541845/ https://www.ncbi.nlm.nih.gov/pubmed/26140347 http://dx.doi.org/10.3390/s150715578 |
work_keys_str_mv | AT yangxiaoling spectralandimageintegratedanalysisofhyperspectraldataforwaxycornseedvarietyclassification AT honghanmei spectralandimageintegratedanalysisofhyperspectraldataforwaxycornseedvarietyclassification AT youzhaohong spectralandimageintegratedanalysisofhyperspectraldataforwaxycornseedvarietyclassification AT chengfang spectralandimageintegratedanalysisofhyperspectraldataforwaxycornseedvarietyclassification |