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Byoung-Kwan Cho     Institute, Department or Faculty Head 
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Byoung-Kwan Cho published an article in February 2019.
Top co-authors See all
Moon S. Kim

195 shared publications

USDA-ARS Environmental Microbial and Food Safety Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Beltsville, MD 20705, USA

Lalit Mohan Kandpal

20 shared publications

Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea

Insuck Baek

9 shared publications

Department of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA

Dewi Kusumaningrum

4 shared publications

Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea

Santosh Lohumi

3 shared publications

Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea

156
Publications
30
Reads
0
Downloads
148
Citations
Publication Record
Distribution of Articles published per year 
(2005 - 2019)
Total number of journals
published in
 
29
 
Publications See all
Article 1 Read 0 Citations Line-scan imaging analysis for rapid viability evaluation of white-fertilized-egg embryos Eunsoo Park, Santosh Lohumi, Byoung-Kwan Cho Published: 01 February 2019
Sensors and Actuators B: Chemical, doi: 10.1016/j.snb.2018.10.109
DOI See at publisher website
Article 0 Reads 0 Citations Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis Insuck Baek, Dewi Kusumaningrum, Lalit Mohan Kandpal, Santos... Published: 11 January 2019
Sensors, doi: 10.3390/s19020271
DOI See at publisher website ABS Show/hide abstract
Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR–HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability.
Article 0 Reads 0 Citations Rapid Measurement of Soybean Seed Viability Using Kernel-Based Multispectral Image Analysis. Insuck Baek, Dewi Kusumaningrum, Lalit Mohan Kandpal, Santos... Published: 11 January 2019
Sensors,
PubMed View at PubMed ABS Show/hide abstract
Viability is an important quality factor influencing seed germination and crop yield. Current seed-viability testing methods rely on conventional manual inspections, which use destructive, labor-intensive and time-consuming measurements. The aim of this study is to distinguish between viable and nonviable soybean seeds, using a near-infrared (NIR) hyperspectral imaging (HSI) technique in a rapid and nondestructive manner. The data extracted from the NIR⁻HSI of viable and nonviable soybean seeds were analyzed using a partial least-squares discrimination analysis (PLS-DA) technique for classifying the viable and nonviable soybean seeds. Variable importance in projection (VIP) was used as a waveband selection method to develop a multispectral imaging model. Initially, the spectral profile of each pixel in the soybean seed images was subjected to PLS-DA analysis, which yielded a reasonable classification accuracy; however, the pixel-based classification method was not successful for high accuracy detection for nonviable seeds. Another viability detection method was then investigated: a kernel image threshold method with an optimum-detection-rate strategy. The kernel-based classification of seeds showed over 95% accuracy even when using only seven optimal wavebands selected through VIP. The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability.
Article 0 Reads 0 Citations X-ray CT image analysis for morphology of muskmelon seed in relation to germination Mohammed Raju Ahmed, Jannat Yasmin, Wakholi Collins, Byoung-... Published: 01 November 2018
Biosystems Engineering, doi: 10.1016/j.biosystemseng.2018.09.015
DOI See at publisher website
Article 0 Reads 0 Citations Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique Santosh Lohumi, Collins Wakholi, Jong Ho Baek, Byeoung Do Ki... Published: 31 October 2018
Korean journal for food science of animal resources, doi: 10.5851/kosfa.2018.e44
DOI See at publisher website
Article 0 Reads 0 Citations Through-packaging analysis of butter adulteration using line-scan spatially offset Raman spectroscopy Santosh Lohumi, Hoonsoo Lee, Moon S. Kim, Jianwei Qin, Byoun... Published: 22 June 2018
Analytical and Bioanalytical Chemistry, doi: 10.1007/s00216-018-1189-1
DOI See at publisher website
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