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Sensors 2018, 18(3), 813; https://doi.org/10.3390/s18030813

A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds

1
Department of Plant Genetics and Breeding, College of Agriculture, China Agricultural University/Beijing Key Laboratory of Crop Genetic Improvement/The Innovation Center (Beijing) of Crop Seed Sciences Ministry of Agriculture, Beijing 100193, China
2
National R&D Center for Agro-Processing Equipments, College of Engineering, China Agricultural University, Beijing 100083, China
3
College of Science, China Agricultural University, Beijing 100083, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 15 January 2018 / Revised: 5 March 2018 / Accepted: 6 March 2018 / Published: 8 March 2018
(This article belongs to the Section Remote Sensors)
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Abstract

This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400–1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides’ spectra of every seed), and mixture datasets (two sides’ spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds. Our results showed that the standard normal variate (SNV)-SPA-PLS-DA model had high classification accuracy for whole seeds (>85.2%) and for viable seeds (>89.5%), and that the prediction set was based on a mixed spectral dataset by only using 16 wavebands. After screening with this model, the final germination of the seed lot could be higher than 89.5%. Here, we develop a reliable methodology for predicting the viability of wheat seeds, showing that the VIS/NIR hyperspectral imaging is an accurate technique for the classification of viable and non-viable wheat seeds in a non-destructive manner. View Full-Text
Keywords: hyperspectral imaging; seed viability; dataset; PLS-DA; SVM hyperspectral imaging; seed viability; dataset; PLS-DA; SVM
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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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Zhang, T.; Wei, W.; Zhao, B.; Wang, R.; Li, M.; Yang, L.; Wang, J.; Sun, Q. A Reliable Methodology for Determining Seed Viability by Using Hyperspectral Data from Two Sides of Wheat Seeds. Sensors 2018, 18, 813.

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