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Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds

1
Department of Mechanical Engineering, University of Maryland-Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA
2
USDA-ARS Environmental Microbial and Food Safety Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Beltsville, MD 20705, USA
3
Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehar-ro, Yuseong-gu, Daejeon 34134, Korea
4
National Institute of Agricultural Sciences, Rural Development Administration, 310 Nonsaengmyeong-ro, Wansan-gu, Jeonju-si, Jeollabuk-do 54875, Korea
5
Department of Biosystems Engineering, College of Agricultural and Life Sciences, Kangwon National University, 1 Gangwondaehakgil, Chuncheon-Si, Gangwon-Do 24341, Korea
6
USDA-ARS Dale Bumpers National Rice Research Center, Stuttgart, AR 72160, USA
7
Department of Food Bio Science, College of Biomedical and Health Science, Konkuk University, Chungju 27478, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(5), 1027; https://doi.org/10.3390/app9051027
Received: 1 January 2019 / Revised: 26 February 2019 / Accepted: 5 March 2019 / Published: 12 March 2019
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture II)
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Abstract

The inspection of rice grain that may be infected by seedborne disease is important for ensuring uniform plant stands in production fields as well as preventing proliferation of some seedborne diseases. The goal of this study was to use a hyperspectral imaging (HSI) technique to find optimal wavelengths and develop a model for detecting discolored, diseased rice seed infected by bacterial panicle blight (Burkholderia glumae), a seedborne pathogen. For this purpose, the HSI data spanning the visible/near-infrared wavelength region between 400 and 1000 nm were collected for 500 sound and discolored rice seeds. For selecting optimal wavelengths to use for detecting diseased seed, a sequential forward selection (SFS) method combined with various spectral pretreatments was employed. To evaluate performance based on optimal wavelengths, support vector machine (SVM) and linear and quadratic discriminant analysis (LDA and QDA) models were developed for detection of discolored seeds. As a result, the violet and red regions of the visible spectrum were selected as key wavelengths reflecting the characteristics of the discolored rice seeds. When using only two or only three selected wavelengths, all of the classification methods achieved high classification accuracies over 90% for both the calibration and validation sample sets. The results of the study showed that only two to three wavelengths are needed to differentiate between discolored, diseased and sound rice, instead of using the entire HSI wavelength regions. This demonstrates the feasibility of developing a low cost multispectral imaging technology based on these selected wavelengths for non-destructive and high-throughput screening of diseased rice seed. View Full-Text
Keywords: diseased seed; hyperspectral imaging; SVM; LDA; QDA; image processing diseased seed; hyperspectral imaging; SVM; LDA; QDA; image processing
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Baek, I.; Kim, M.S.; Cho, B.-K.; Mo, C.; Barnaby, J.Y.; McClung, A.M.; Oh, M. Selection of Optimal Hyperspectral Wavebands for Detection of Discolored, Diseased Rice Seeds. Appl. Sci. 2019, 9, 1027.

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