Title
Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging.
Abstract
Rapid visible/near-infrared (VNIR) hyperspectral imaging methods, employing both a single waveband algorithm and multi-spectral algorithms, were developed in order to discrimination between sound and discolored lettuce. Reflectance spectra for sound and discolored lettuce surfaces were extracted from hyperspectral reflectance images obtained in the 400-1000 nm wavelength range. The optimal wavebands for discriminating between discolored and sound lettuce surfaces were determined using one-way analysis of variance. Multi-spectral imaging algorithms developed using ratio and subtraction functions resulted in enhanced classification accuracy of above 99.9% for discolored and sound areas on both adaxial and abaxial lettuce surfaces. Ratio imaging (RI) and subtraction imaging (SI) algorithms at wavelengths of 552/701 nm and 557-701 nm, respectively, exhibited better classification performances compared to results obtained for all possible two-waveband combinations. These results suggest that hyperspectral reflectance imaging techniques can potentially be used to discriminate between discolored and sound fresh-cut lettuce.
Year
DOI
Venue
2015
10.3390/s151129511
SENSORS
Keywords
Field
DocType
hyperspectral imaging,multispectral imaging,lettuce,discoloration,image processing
VNIR,Hyperspectral reflectance,Remote sensing,Multispectral image,Image processing,Hyperspectral imaging,Engineering,Reflectivity,Wavelength
Journal
Volume
Issue
ISSN
15
11.0
1424-8220
Citations 
PageRank 
References 
3
0.42
0
Authors
6
Name
Order
Citations
PageRank
Changyeun Mo182.91
Giyoung Kim2114.02
Jongguk Lim383.59
Moon S Kim4123.87
Hyunjeong Cho530.42
Byoung-Kwan Cho6163.16