Next Article in Journal
Synthesis of ZnMn2O4 Nanoparticles by a Microwave-Assisted Colloidal Method and their Evaluation as a Gas Sensor of Propane and Carbon Monoxide
Next Article in Special Issue
Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis
Previous Article in Journal
Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors
Previous Article in Special Issue
A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(3), 700; https://doi.org/10.3390/s18030700

A Micro-Damage Detection Method of Litchi Fruit Using Hyperspectral Imaging Technology

College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Received: 17 January 2018 / Revised: 22 February 2018 / Accepted: 23 February 2018 / Published: 26 February 2018
(This article belongs to the Special Issue Spectroscopy Based Sensors)
Full-Text   |   PDF [3542 KB, uploaded 26 February 2018]   |  

Abstract

The non-destructive testing of litchi fruit is of great significance to the fresh-keeping, storage and transportation of harvested litchis. To achieve quick and accurate micro-damage detection, a non-destructive grading test method for litchi fruits was studied using 400–1000 nm hyperspectral imaging technology. The Huaizhi litchi was chosen in this study, and the hyperspectral data average for the region of interest (ROI) of litchi fruit was extracted for spectral data analysis. Then the hyperspectral data samples of fresh and micro-damaged litchi fruits were selected, and a partial least squares discriminant analysis (PLS-DA) was used to establish a prediction model for the realization of qualitative analysis for litchis with different qualities. For the external validation set, the mean per-type recall and precision were 94.10% and 93.95%, respectively. Principal component analysis (PCA) was used to determine the sensitive wavelength for recognition of litchi quality characteristics, with the results of wavelengths corresponding to the local extremum for the weight coefficient of PC3, i.e., 694, 725 and 798 nm. Then the single-band images corresponding to each sensitive wavelength were analyzed. Finally, the 7-dimension features of the PC3 image were extracted using the Gray Level Co-occurrence Matrix (GLCM). Through image processing, least squares support vector machine (LS-SVM) modeling was conducted to classify the different qualities of litchis. The model was validated using the experiment data, and the average accuracy of the validation set was 93.75%, while the external validation set was 95%. The results indicate the feasibility of using hyperspectral imaging technology in litchi postpartum non-destructive detection and classification. View Full-Text
Keywords: hyperspectral imaging; litchi quality; micro-damages detection; image recognition hyperspectral imaging; litchi quality; micro-damages detection; image recognition
Figures

Figure 1

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Xiong, J.; Lin, R.; Bu, R.; Liu, Z.; Yang, Z.; Yu, L. A Micro-Damage Detection Method of Litchi Fruit Using Hyperspectral Imaging Technology. Sensors 2018, 18, 700.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top