Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

Apple Internal Quality Inspection Using Hyperspectral Image Technology

Authors
Xiao-Yan Chen, Wen-Tao Chen, Jia-Sui Lv, Xiang Long, Tao Pang
Corresponding Author
Xiao-Yan Chen
Available Online December 2016.
DOI
https://doi.org/10.2991/icwcsn-16.2017.155How to use a DOI?
Keywords
Hyperspectral Images, Sugar Content, Firmness, Artificial Neural Network
Abstract
The internal parameters are important indexes for detecting the quality of the apples. This paper extracted spectral values of the apples from 400-1000nm with the hyperspectral image technology, carried out pre-treatment to original spectrums with MSC, performed regression analysis on spectral reflectivity of sugar content and firmness, and finally established prediction model of apple sugar content and firmness with BP (back propagation) artificial neural network. The results show that the correlation coefficient of the prediction model for sugar content is 0.9861, the average error is 0.118øBrix; the correlation coefficient of the prediction model for firmness is 0.9771, the average error is 0.054Kg/cm^2. Therefore, it is feasible to detect the internal quality parameter of apples using hyperspectral technology.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
Part of series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-302-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icwcsn-16.2017.155How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xiao-Yan Chen
AU  - Wen-Tao Chen
AU  - Jia-Sui Lv
AU  - Xiang Long
AU  - Tao Pang
PY  - 2016/12
DA  - 2016/12
TI  - Apple Internal Quality Inspection Using Hyperspectral Image Technology
BT  - 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icwcsn-16.2017.155
DO  - https://doi.org/10.2991/icwcsn-16.2017.155
ID  - Chen2016/12
ER  -