Apple Internal Quality Inspection Using Hyperspectral Image Technology
Xiao-Yan Chen, Wen-Tao Chen, Jia-Sui Lv, Xiang Long, Tao Pang
Available Online December 2016.
- https://doi.org/10.2991/icwcsn-16.2017.155How to use a DOI?
- Hyperspectral Images, Sugar Content, Firmness, Artificial Neural Network
- 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.
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 -