2 June 2011 Hyperspectral near-infrared reflectance imaging for detection of defect tomatoes
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Abstract
Cuticle cracks on tomatoes are potential sites of pathogenic infection that may cause deleterious consequences both to consumer health and to fresh and fresh-cut produce markets. The feasibility of hyperspectral near-infrared imaging technique in the spectral range of 1000 nm to 1700 nm was investigated for detecting defects on tomatoes. Spectral information obtained from the regions of interest on both defect areas and sound areas were analyzed to determine some an optimal waveband ratio that could be used for further image processing to discriminate defect areas from the sound tomato surfaces. Unsupervised multivariate analysis method, such as principal component analysis, was also explored to improve detection accuracy. Threshold values for the optimized features were determined using linear discriminant analysis. Results showed that tomatoes with defects could be differentiated from the sound ones, with an overall accuracy of 94.4%. The spectral wavebands and image processing algorithms determined in this study could be used for multispectral inspection of defects tomatoes.
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Hoonsoo Lee, Hoonsoo Lee, Moon S. Kim, Moon S. Kim, Danhee Jeong, Danhee Jeong, Kuanglin Chao, Kuanglin Chao, Byoung-Kwan Cho, Byoung-Kwan Cho, Stephen R. Delwiche, Stephen R. Delwiche, } "Hyperspectral near-infrared reflectance imaging for detection of defect tomatoes", Proc. SPIE 8027, Sensing for Agriculture and Food Quality and Safety III, 80270J (2 June 2011); doi: 10.1117/12.888098; https://doi.org/10.1117/12.888098
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