Detection of Lettuce Discoloration Using Hyperspectral Reflectance Imaging

Sensors

Byoung-Kwan Cho¹  Moon S. Kim²  Hyunjeong Cho³  Giyoung Kim⁴  Changyeun Mo⁴  Jongguk Lim⁴ 

[1] Research Institute, National Agricultural Products Quality Management Service, 141 Yongjeon-ro, Gimcheon-si, Gyeongsangbuk-do 39660, Korea;Environmental Microbiology and Food Safety Laboratory, BARC-East, Agricultural Research Service, US Department of Agriculture, 10300 Baltimore Avenue, Beltsville, MD 20705, USA;;Experiment &National Institute of Agricultural Science, Rural Development Administration, 310 Nonsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 54875, Korea;

关键词: hyperspectral imaging;    multispectral imaging;    lettuce;    discoloration;    image processing;   

DOI  :  10.3390/s151129511

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown