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

150 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

10 shared publications

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

Santosh Lohumi

6 shared publications

Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea;(J.Y.);(M.R.A.);(S.L.);(C.W.)

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

157
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Publication Record
Distribution of Articles published per year 
(2006 - 2019)
Total number of journals
published in
 
24
 
Publications See all
Article 0 Reads 0 Citations Classification Method for Viability Screening of Naturally Aged Watermelon Seeds Using FT-NIR Spectroscopy Jannat Yasmin, Mohammed Raju Ahmed, Santosh Lohumi, Collins ... Published: 08 March 2019
Sensors, doi: 10.3390/s19051190
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Viability analysis of stored seeds before sowing has a great importance as plant seeds lose their viability when they exposed to long term storage. In this study, the potential of Fourier transform near infrared spectroscopy (FT-NIR) was investigated to discriminate between viable and non-viable triploid watermelon seeds of three different varieties stored for four years (natural aging) in controlled conditions. Because of the thick seed-coat of triploid watermelon seeds, penetration depth of FT-NIR light source was first confirmed to ensure seed embryo spectra can be collected effectively. The collected spectral data were divided into viable and nonviable groups after the viability being confirmed by conducting a standard germination test. The obtained results showed that the developed partial least discriminant analysis (PLS-DA) model had high classification accuracy where the dataset was made after mixing three different varieties of watermelon seeds. Finally, developed model was evaluated with an external data set (collected at different time) of hundred samples selected randomly from three varieties. The results yield a good classification accuracy for both viable (87.7%) and nonviable seeds (82%), thus the developed model can be considered as a “general model” since it can be applied to three different varieties of seeds and data collected at different time.
Article 0 Reads 0 Citations Development of a Low-Cost Multi-Waveband LED Illumination Imaging Technique for Rapid Evaluation of Fresh Meat Quality Lalit Mohan Kandpal, Jayoung Lee, Jihoon Bae, Santosh Lohumi... Published: 04 March 2019
Applied Sciences, doi: 10.3390/app9050912
DOI See at publisher website ABS Show/hide abstract
Determining the quality of meat has always been essential for the food industry because consumers prefer superior quality meat. Therefore, the food industry requires the development of a rapid and non-destructive method for meat-quality determination. Over the past few years, a number of techniques have been presented for monitoring meat–chemical attributes. However, most previous techniques are quite expensive, destructive, and require complex hardware to operate. Thus, in this work, we demonstrate a low-cost sensing technique (eliminating the expensive equipment and complicated design) for meat–chemical quality detection. The newly developed system was integrated with a low-cost monochrome camera and ordinary light-emitting diode (LED) light sources, with fifteen different wavebands ranging from 458 to 950 nm. The monochrome camera captures images of the meat sample across a spectral range from 458 to 950 nm using a single snapshot method. The chemical values (e.g., moisture, fat, and protein) were also determined using conventional methods. The collected images were combined to produce a multispectral data cube and to extract spectral data. Partial least squares (PLS) and support vector regression (SVR) modeling were used on the extracted spectra and chemical values. The developed models for meat samples displayed accurate chemical-component prediction (R2 > 0.80). Our model, based on a monochrome sensor using only fifteen wavebands, provided reasonable results compared with the previously developed expensive spectroscopic techniques. Therefore, this complementary metal-oxide semiconductor (CMOS) based multispectral sensing technique may have the potential to detect meat quality, thereby facilitating a simple, fast, and cost-effective method applicable to small-scale meat-processing industries.
Article 2 Reads 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,
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 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 1 Read 1 Citation 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
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