Journal
BIOSYSTEMS ENGINEERING
Volume 148, Issue -, Pages 138-147Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2016.05.014
Keywords
Cucumber green mottle mosaic virus (CGMMV); Watermelon seeds; Near-infrared (NIR) hyperspectral imaging; Partial least square discriminant analysis (PLS-DA); Least square support vector machine (LS-SVM)
Funding
- Technology Commercialization Support Program
- Ministry of Agriculture, Food and Rural Affairs (MAFRA)
- Next Generation BioGreen 21 Program, Rural Development Administration, Republic of Korea [PJ01125602]
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The cucurbit diseases caused by cucumber green mottle mosaic virus (CGMMV) have led to a serious problem to growers and seed producers because it is difficult to prevent spreading through pathogen-infected seeds. Conventional detection methods for infected seeds such as biological, serological, and molecular measurements are not practical for measuring entire samples due to their destructive nature, and time, and cost issues. For this reason, it is necessary to develop a rapid and non-destructive novel technique for detecting seeds infestation. A near-infrared (NIR) hyperspectral imaging system was used to discriminate virus-infected seeds from healthy seeds with partial least square discriminant analysis (PLS-DA) and least square support vector machine (LS-SVM). The classification accuracy for virus-infected watermelon seeds were 83.3% with the best model, demonstrating the potential of NIR hyperspectral imaging for detection of virus-infected watermelon seeds. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.
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