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

Sensors
Juntao XiongLianyi Yu

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 feat...Continue Reading

Citations

Feb 16, 2021·Critical Reviews in Food Science and Nutrition·Yong HeChu Zhang
Sep 2, 2021·Annual Review of Biomedical Data Science·George-John NychasFady Mohareb

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Methods Mentioned

BETA
imaging technology
imaging technique
PCA

Software Mentioned

Unscrambler
GLCM
Matlab
Excel
DA
Spectra SENS
ENVI
PLS

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