Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network-Reference-Cited by-同舟云学术

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

Published:2019 Issue:22 Volume:9 Page:12635-12644
ISSN:2046-2069
Container-title:RSC Advances
language:en
Short-container-title:RSC Adv.

Author:

Wu Na¹²³⁴⁵^ORCID,Zhang Yu⁶⁷⁴,Na Risu⁸⁹⁴,Mi Chunxiao¹²³⁴⁵,Zhu Susu¹²³⁴⁵,He Yong¹²³⁴⁵,Zhang Chu¹²³⁴⁵

Affiliation:

1. College of Biosystems Engineering and Food Science

2. Zhejiang University

3. Hangzhou 310058

4. China

5. State Key Laboratory of Modern Optical Instrumentation

6. Zhejiang Technical Institute of Economics

7. Hangzhou 310018

8. Chifeng Academy of Agricultural and Animal Sciences

9. Chifeng 024031

Abstract

The excellent representation ability of deep spectral features enables hyperspectral imaging combined with deep convolutional neural network to be a powerful tool for large-scale seeds detection in modern seed industry.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2019/RA/C8RA10335F

Reference34 articles.

1. Viscosity of beta-glucan in oat products

2. Protein From Oat

3. Review of analytical methods for measurement of oat proteins: The need for standardized methods

4. Genetic purity testing of F1 hybrid seed with molecular markers in cabbage (Brassica oleracea var. capitata)

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