Apple Internal Quality Inspection Using Hyperspectral Image Technology

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
PB  - Atlantis Press
SP  - 768
EP  - 774
SN  - 2352-538X
UR  - https://doi.org/10.2991/icwcsn-16.2017.155
DO  - 10.2991/icwcsn-16.2017.155
ID  - Chen2016/12
ER  -