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Appl. Sci. 2016, 6(6), 183; doi:10.3390/app6060183

Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging

1
Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi, Jiangsu 214122, China
2
USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Bldg., 303, BARC-East, 10300 Baltimore Ave., MD 20705-2350, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Fernández-Caballero
Received: 18 May 2016 / Revised: 12 June 2016 / Accepted: 14 June 2016 / Published: 21 June 2016
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
View Full-Text   |   Download PDF [2237 KB, uploaded 21 June 2016]   |  

Abstract

Hyperspectral imaging (HSI) technology has been extensively studied in the classification of seed variety. A novel procedure for the classification of maize seed varieties based on HSI was proposed in this study. The optimal wavelengths for the classification of maize seed varieties were selected using the successive projections algorithm (SPA) to improve the acquiring and processing speed of HSI. Subsequently, spectral and imaging features were extracted from regions of interest of the hyperspectral images. Principle component analysis and multidimensional scaling were then introduced to transform/reduce the classification features for overcoming the risk of dimension disaster caused by the use of a large number of features. Finally, the integrating features were used to develop a least squares–support vector machines (LS–SVM) model. The LS–SVM model, using the integration of spectral and image features combined with feature transformation methods, achieved more than 90% of test accuracy, which was better than the 83.68% obtained by model using the original spectral and image features, and much higher than the 76.18% obtained by the model only using the spectral features. This procedure provides a possible way to apply the multispectral imaging system to classify seed varieties with high accuracy. View Full-Text
Keywords: hyperspectral imaging; maize seed; classification; SPA; LS–SVM; feature transformation methods hyperspectral imaging; maize seed; classification; SPA; LS–SVM; feature transformation methods
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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).

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MDPI and ACS Style

Huang, M.; He, C.; Zhu, Q.; Qin, J. Maize Seed Variety Classification Using the Integration of Spectral and Image Features Combined with Feature Transformation Based on Hyperspectral Imaging. Appl. Sci. 2016, 6, 183.

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