• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.4
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• pp.75-86
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• 2020

The purpose of this study is to develop a technology for estimating rice growth and damage effect according to bacterial leaf blight using UAV multi-spectral imagery. For this purpose, we analyzed the change of aerial images, rice growth factors (plant height, dry weight, LAI) and disease effects according to disease occurrence by using UAV images for 3 rice varieties (Milyang23, Sindongjin-byeo, Saenuri-byeo) from 2017 to 2018. The correlation between vegetation index and rice growth factor during vegetative growth period showed a high value of 0.9 or higher each year. As a result of applying the growth estimation model built in 2017 to 2018, the plant height of Milyang23 showed good error withing 10%. However, it is considered that studies to improve the accuracy of other items are needed. Fixed wing unmanned aerial photographs were also possible to estimate the damage area after 2 to 4 weeks from inoculation. Although sensing data in the multi-spectral (Blue, Green, Red, NIR) band have limitations in early diagnosis of rice disease, for rice varieties such as Milyang23 and Sindongjin-byeo, it was possible to construct the equation of infected leaf area ratio and rice yield estimation using UAV imagery in early and mid-September with high correlation coefficient of 0.8 to 0.9. The results of this study are expected to be useful for farming and policy support related to estimating rice growth, rice plant disease and yield change based on UAV images.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.143-154
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• 2004

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multi-spectral automatic Aerial photographic system(PKNU 2). This system's Multi-spectral camera can catch the visible(RGB) and infrared(NIR) bands($3032{\times}2008$ pixels) image. Visible and infrared bands images were obtained from each camera respectively and produced Color-infrared composite images to be analyzed in the purpose of the environment monitor but that was not very good data. Moreover, it has a demerit that the stereoscopic overlap area is not satisfied with 60% due to the 12s storage time of each data, while it was possible that PKNU 2 system photographed photos of great capacity. Therefore, we have been developing the advanced PKNU 2(PKNU 3) that consists of color-infrared spectral camera can photograph the visible and near infrared bands data using one sensor at once, thermal infrared camera, two of 40 G computers to store images, and MPEG board to compress and transfer data to the computer at the real time and can attach and detach itself to a helicopter. Verification and calibration of each sensor(REDLAKE MS 4000, Raytheon IRPro) were conducted before we took the aerial photographs for obtaining more valuable data. Corrections for the spectral characteristics and radial lens distortions of sensor were carried out.

• Resources Recycling
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• v.26 no.6
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• pp.73-83
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• 2017

Used small household appliances have a wide variety of product types and component materials, and contain high percentage of black plastics. However, they are not being recycled efficiently as conventional sensors such as near-infrared ray (NIR), etc. are not able to detect black plastic by types. In the present study, an automatic sorting system was developed based on laser-induced breakdown spectroscopy (LIBS) to promote the recycling of waste plastics. The system we developed mainly consists of sample feeder, automatic position recognition system, LIBS device, separator and control unit. By applying laser pulse on the target sample, characteristic spectral data can be obtained and analyzed by using CCD detectors. The obtained data was then treated by using a classifier, which was developed based on artificial intelligent algorithm. The separation tests on waste plastics also were carried out by using a lab-scale automatic sorting system and the test results will be discussed. The classification rate of the radial basis neural network (RBFNNs) classifier developed in this study was about > 97%. The recognition rate of the black plastic by types with the automatic sorting system was more than 94.0% and the sorting efficiency was more than 80.0%. Automatic sorting system based on LIBS technology is in its infant stage and it has a high potential for utilization in and outside Korea due to its excellent economic efficiency.

• Journal of The Korean Society of Grassland and Forage Science
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• v.31 no.4
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• pp.409-414
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• 2011

This study was conducted to find out an alternative way of rapid and accurate analysis of forage quality. Near Infrared Reflectance Spectroscopy (NIRS) was used to evaluate the possibility of forage analysis. 175 samples consisted of Italian ryegrass, whole crop barley and pea seeded spring in 2009 were collected. The samples were analyzed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF), and neutral detergent fiber (NDF), and also scanned using NIRSystem with wavelength from 400~2,500 nm. Multiple linear regression was used with wet analysis data for developing the calibration model and validated unknown samples. The important index in this experiment were SEC, SEP. The r2 value for moisture, CP, CA, ADF, and NDF in calibration set was 0.65, 0.97, 0.93, 0.99, and 0.97 and also was 0.15, 0.94, 0.96, 0.98 and 0.98 in validation set, respectively. The results of this experiment indicates that NIRS was reliable analytical method to assess forage quality for CP, CA ADF and NDF except moisture content in forage when proper samples incorporated into the equation development.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.117-124
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• 2002

This research was conducted to develop a method to remove the effect of surface temperature of Shingo pears for sugar content measurement. Sugar content was measured by a near-infrared spectrum analysis technique. Reflected spectrum and sugar content of a pear were used for developing regression models. For the model development, reflected spectrums having wavelengths in the range of 654 to 1,052nm were used. To remove the effect of surface temperature, special sample preparation techniques and partial least square (PLS) regression models were proposed and tested. 71 Shingo pears stored in a cold storage, which had 2$^{\circ}C$ inside temperature, were taken out and left in a room temperature for a while. Temperature and reflected spectrum of each pear was measured. To increase the temperature distribution of samples, temperature and reflected spectrum of each pear was measured four times with one hour twenty minutes interval. During the experiment, temperature of pears increased up to 17 $^{\circ}C$. The total number of measured spectrum was 284. Three groups of spectrum data were formed according to temperature distribution. First group had surface temperature of 14$^{\circ}C$ and total number of 51. Second group consisted of the first and the fourth experiment data which contained the minimum and the maximum temperatures. Third group consisted of 155 data with normal temperature-distribution. The rest data set were used for model evaluation. Results shelved that PLS model I, which was developed by using the first data group, was inadequate for measuring sugar content of pears which had different surface temperatures from 14$^{\circ}C$. After temperature compensation, sugar content predictions became close to the measured values. Since using many data which had wide range of surface temperatures, PLS model II and III were able to predict sugar content of pears without additional temperature compensation. PLS model IV, which included the surface temperatures as an independent variable. showed slightly improved performance(R$^2$=0.73). Performance of the model could be enhanced by using samples with more wide range of temperatures and sugar contents.

• Resources Recycling
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• v.26 no.1
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• pp.69-77
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• 2017

Used small household appliances(small e-waste) consists of a variety of complex materials and components. The small e-waste is mainly composed of plastics and an important potential source of waste plastic. The black plastics, particularly are very difficult to separate by resin type and therefore these are mainly recycled in the form of a mixtures. In the present study, the sorting technologies such as gravity and electro static separation, near-infrared ray(NIR) and IR/Raman optical sorting separation on mixture of black plastics were analyzed and their limitations on sorting process were also investigated. The Laser Induced Breakdown Spectroscopy(LIBS) spectrum of each black plastics was used for identification of black plastics by resin type, and after analyzing the normalization operation, Principal Component Analysis(PCA) was carried out. The spectrum data was optimized through PCA process. In order to improve the identification accuracy and sorting efficiency of black plastics, it is necessary to design a classifier with high efficiency and to improve the performance and reliability of the classifier by applying the field of intelligent algorithms.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.61-66
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• 2022

Sesamin and sesamolin are major lignan components with a wide range of potential biological activities of sesame seeds. Near infrared reflectance spectroscopy (NIRS) is a rapid and non-destructive analysis method widely used for the quantitative determination of major components in many agricultural products. This study was conducted to develop a screening method to determine the lignan contents for sesame breeding. Sesamin and sesamolin contents of 482 sesame samples ranged from 0.03-14.40 mg/g and 0.10-3.79 mg/g with an average of 4.93 mg/g and 1.74 mg/g, respectively. Each sample was scanned using NIRS and calculated for the calibration and validation equations. The optimal performance calibration model was obtained from the original spectra using partial least squares (PLS). The coefficient of determination in calibration (R²) and standard error of calibration (SEC) were 0.963 and 0.861 for sesamin and 0.875 and 0.292 for sesamolin, respectively. Cross-validation results of the NIRS equation showed an R² of 0.889 in the prediction for sesamin and 0.781 for sesamolin and a standard error of cross-validation (SECV) of 1.163 for sesamin and 0.417 for sesamolin. The results showed that the NIRS equation for sesamin and sesamolin could be effective in selecting high lignan sesame lines in early generations of sesame breeding.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.6
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• pp.219-224
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• 2022

CaF₂ single crystal has a large band gap (12 eV), and it is used for optical windows, prisms, and lenses due to its excellent transmittance in a wide wavelength range and low refractive index. Moreover, it is expected to be one of the materials for ultraviolet transmissive laser optical components. CaF₂ belongs to the fluoride compounds and has a face-centered cubic (FCC) structure with three sub-lattices. The representative method for CaF₂ single crystal growth is Czochralski, which method has the advantages of high production efficiency and the ability to make large crystals. In this study, X-ray diffraction (XRD), X-ray rocking curves (XRC) measurement, and chemical etching were performed to analyze the crystallinity and defect density of the CaF₂ single crystals, grown by the Czochralski method. Fourier-transform infrared spectroscopy (FT-IR) and UV-VIS-NIR spectroscopy systems were used to investigate the optical properties of the CaF₂ crystal. The provability of various applications, including UV application, was systematically investigated with various analysis results.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.6
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• pp.716-722
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• 1995

Near Infrared Reflectance Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of small grain and forage quality analysis. The objective of this study was to establish the rapid, easy and accurate analysis method for major components of covered barley using NIRS system. NIRS used in this study was filter type instrument, Neotec 102. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Standard errors of prediction (SEP) and simple correlations for unknown samples were calculated using obtained equation. SEPs for starch, $\beta$-glucan, protein and ash contents were 2.75%, 0.64%, 0.26% and 0.19%, respectively. The simple correlations for starch, $\beta$-glucan, protein and ash contents were 0.932, 0.588, 0.984 and 0.867, respectively. It was concluded that the NIRS method would be applicabl for the rapid determination of starch, protein and ash contents in barley grains.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1513-1513
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• 2001

Present Food Legislation compels dairy industry to carry out analyses in order to guarantee the food safety and quality of products. Furthermore, in many cases industry pays milk according to bacteriological or/and nutritional quality. In order to do these analyses, several expensive instruments are needed (Milkoscan, Fossomatic, Bactoscan). NIRS technology Provides a unique instrument to deal with all analytical requirements. It offers as main advantages its speed and, specially, its versatility, since not only allows determine all the parameters required in milk analysis, but also allows analyse other dairy products, like cheese or whey. The objective of this study is to develop NIRS calibration equations to predict several quality parameters in goat milk, cheese and whey. Three sets of 123 milk samples, 190 cheese samples and 109 whey samples, have been analysed in a FOSS NIR Systems 6500 I spectrophotometer equipped with a spinning module. Milk and whey were analysed by folded transmission, using circular cells with gold surface and pathlength of 0.1 m, while intact cheese was analysed by reflectance using standard circular cells. NIRS calibrations were obtained for the prediction of chemical composition in goat milk, for fat (r$^2$=0.92; SECV=0.20%), total solids (r$^2$=0.95: SECV=0.22%), protein (r$^2$=0.94; SECV=0.07%), casein (r$^2$=0.93; SECV=0.07%) and lactose (r$^2$=0.89; SECV=0.05%). Moreover, equations have been performed to determine somatic cells (r$^2$=0.81; SECV=276.89%) and total bacteria (r$^2$=0.58; SECV=499.32%) counts in goat milk. In the case of cheese, calibrations were obtained for the prediction of fat (r$^2$=0.92; SECV=0.57), total solids (r$^2$=0.80; SECV=0.92%) and protein (r$^2$=0.70; SECV=0.63%). In whey, fat (r$^2$=0.66; SECV=0.08%), total solids (r$^2$=0.67; SECV=0.19%) and protein (r$^2$=0.76; SECV=0.07%) NIRS equations were obtained. These results proved the viability of NIRS technology to predict chemical and microbiological parameters and somatic cells count in goat milk, as well as chemical composition of goat cheese and whey.