• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.45-56
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• 2024

Faced with aging populations, declining resources, and limited agricultural productivity, rural areas in South Korea require innovative solutions. This study investigated the potential of drone-based vegetation indices (VIs) to analyze soybean growth patterns in open-field smart agriculture in Goesan-gun, Chungbuk Province, South Korea. We monitored multi-seasonal normalized difference vegetation index (NDVI) and the normalized difference red edge (NDRE) data for three soybean lots with different irrigation methods (subsurface drainage, conventional, subsurface drip irrigation) using drone remote sensing. Combining NDVI (photosynthetically active biomass, PAB) and NDRE (chlorophyll) offered a comprehensive analysis of soybean growth, capturing both overall health and stress responses. Our analysis revealed distinct growth patterns for each lot. LotA(subsurface drainage) displayed early vigor and efficient resource utilization (peaking at NDVI 0.971 and NDRE 0.686), likely due to the drainage system. Lot B (conventional cultivation) showed slower growth and potential limitations (peaking at NDVI 0.963 and NDRE 0.681), suggesting resource constraints or stress. Lot C (subsurface drip irrigation) exhibited rapid initial growth but faced later resource limitations(peaking at NDVI 0.970 and NDRE 0.695). By monitoring NDVI and NDRE variations, farmers can gain valuable insights to optimize resource allocation (reducing costs and environmental impact), improve crop yield and quality (maximizing yield potential), and address rural challenges in South Korea. This study demonstrates the promise of drone-based VIs for revitalizing open-field agriculture, boosting farm income, and attracting young talent, ultimately contributing to a more sustainable and prosperous future for rural communities. Further research integrating additional data and investigating physiological mechanisms can lead to even more effective management strategies and a deeper understanding of VI variations for optimized crop performance.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.1
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• pp.61-69
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• 2004

A long-term growth simulation was performed at 99 land units in Yeoncheon county to test the potential adaptability of each land unit for growing soybean cultivars. The land units for soybean cultivation(CZU), each represented by a geographically referenced land patch, were selected based on land use, soil characteristics, and minimum arable land area. Monthly climatic normals for daily maximum and minimum temperature, precipitation, number of rain days and solar radiation were extracted for each CZU from digital climate models(DCM). The DCM grid cells falling within a same CZU were aggregated to make spatially explicit climatic normals relevant to the CZU. A daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CROPGRO-soybean model suitable for 2 domestic soybean cultivars were derived from long-term field observations. Three foreign cultivars with well established parameters were also added to this study, representing maturity groups 3, 4, and 5. Each treatment was simulated with the randomly generated 30 years' daily weather data(from planting to physiological maturity) for 99 land units in Yeoncheon to simulate the growth and yield responses to the inter-annual climate variation. The same model was run with input data from the Crop Experiment Station in Suwon to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for evaluation. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific cultivar. A computer program(MAPSOY) was written to help utilize the results in a decision-making procedure for agrotechnology transfer. transfer.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.878-886
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• 2011

The soybean is one of the oldest cultivated crops in the world. Microwave remote sensing is an important tool because it can penetrate into cloud independent of weather and it can acquire day or night time data. Especially a ground-based polarimetric scatterometer has advantages of monitoring crop conditions continuously with full polarization and different frequencies. In this study, soybean growth parameters and soil moisture were estimated using polarimetric discrimination ratio (PDR) by radar scatterometer. A ground-based polarimetric scatterometer operating at multiple frequencies was used to continuously monitor the soybean growth condition and soil moisture change. It was set up to obtain data automatically every 10 minutes. The temporal trend of the PDR for all bands agreed with the soybean growth data such as fresh weight, Leaf Area Index, Vegetation Water Content, plant height; i.e., increased until about DOY 271 and decreased afterward. Soil moisture lowly related with PDR in all bands during whole growth stage. In contrast, PDR is relative correlated with soil moisture during below LAI 2. We also analyzed the relationship between the PDR of each band and growth data. It was found that L-band PDR is the most correlated with fresh weight (r=0.96), LAI (r=0.91), vegetation water content (r=0.94) and soil moisture (r=0.86). In addition, the relationship between C-, X-band PDR and growth data were moderately correlated ($r{\geq}0.83$) with the exception of the soil moisture. Based on the analysis of the relation between the PDR at L, C, X-band and soybean growth parameters, we predicted the growth parameters and soil moisture using L-band PDR. Overall good agreement has been observed between retrieved growth data and observed growth data. Results from this study show that PDR appear effective to estimate soybean growth parameters and soil moisture.

• Journal of Microbiology and Biotechnology
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• v.21 no.11
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• pp.1127-1142
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• 2011

The aim of this investigation was to select effective Pseudomonas sp. strains that can enhance the productivity of soybean-wheat cropping systems in Vertisols of Central India. Out of 13 strains of Pseudomonas species tested in vitro, only five strains displayed plant growth-promoting (PGP) properties. All the strains significantly increased soil enzyme activities, except acid phosphatase, total system productivity, and nutrient uptake in field evaluation; soil nutrient status was not significantly influenced. Available data indicated that six strains were better than the others. Principal component analysis (PCA) coupled cluster analysis of yield and nutrient data separated these strains into five distinct clusters with only two effective strains, GRP3 and HHRE81 in cluster IV. In spite of single cluster formation by strains GRP3 and HHRE81, they were diverse owing to greater intracluster distance (4.42) between each other. These results suggest that the GRP3 and HHRE81 strains may be used to increase the productivity efficiency of soybean-wheat cropping systems in Vertisols of Central India. Moreover, the PCA coupled cluster analysis tool may help in the selection of other such strains.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.191-201
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• 2011

Soybean has widely grown for its edible bean which has numerous uses. Microwave remote sensing has a great potential over the conventional remote sensing with the visible and infrared spectra due to its all-weather day-and-night imaging capabilities. In this investigation, a ground-based polarimetric scatterometer operating at multiple frequencies was used to continuously monitor the crop conditions of a soybean field. Polarimetric backscatter data at L, C, and X-bands were acquired every 10 minutes on the microwave observations at various soybean stages. The polarimetric scatterometer consists of a vector network analyzer, a microwave switch, radio frequency cables, power unit and a personal computer. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. The backscattering coefficients were calculated from the measured data at incidence angle $40^{\circ}$ and full polarization (HH, VV, HV, VH) by applying the radar equation. The soybean growth data such as leaf area index (LAI), plant height, fresh and dry weight, vegetation water content and pod weight were measured periodically throughout the growth season. We measured the temporal variations of backscattering coefficients of the soybean crop at L, C, and X-bands during a soybean growth period. In the three bands, VV-polarized backscattering coefficients were higher than HH-polarized backscattering coefficients until mid-June, and thereafter HH-polarized backscattering coefficients were higher than VV-, HV-polarized back scattering coefficients. However, the cross-over stage (HH > VV) was different for each frequency: DOY 200 for L-band and DOY 210 for both C and X-bands. The temporal trend of the backscattering coefficients for all bands agreed with the soybean growth data such as LAI, dry weight and plant height; i.e., increased until about DOY 271 and decreased afterward. We plotted the relationship between the backscattering coefficients with three bands and soybean growth parameters. The growth parameters were highly correlated with HH-polarization at L-band (over r=0.92).

• Food Science and Biotechnology
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• v.15 no.5
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• pp.773-777
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• 2006

Growth of Bacillus cereus was assessed during the storage of seasoned soybean sprouts at 0,5, 10, and $15^{\circ}C$. No lag time in its growth curve was observed and thus the specific growth rate of B. cereus in the exponential growth phase was estimated for bootstrapped microbial count data. The distribution of the specific growth rate could be explained by the BetaGeneral distribution function, and temperature dependence was described by the Ratkowsky square root model. The temperature dependence of the growth could be successfully incorporated into the differential equation of microbial growth to predict the B. cereus count on the seasoned soybean sprouts under fluctuating temperature conditions. Safe shelf lives with different probabilities to reach $10^5\;CFU/g$ were presented at four different temperatures, considering the variation in initial contamination and specific growth rate by the Monte Carlo method and 2-step bootstrapping, respectively. Safe shelf lives defined as the time with a probability of less than 0.1% of reaching the critical limit, were 13.4, 5.2, 3.6, and 2.8 days at 0, 5, 10, and $15^{\circ}C$, respectively.

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.1
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• pp.50-55
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• 2017

In attempt to avoid crop damage through wild bird's picking, this study was designed with aim to evaluate several pre-sowing soybean seed coatings for optimum yield in corn-soybean mixed forage. It was investigated under four cropping treatments, viz. 1) corn sole, 2) corn mixed with soybean without any coating, 3) corn with iron coated soybean and 4) corn with thiram coated soybean. Each treatment had three replicates and corn sole was control treatment. Pioneer (P1184) and crossbred ($PI483463{\times}Hutcheson$) seeds were used for corn and soybean, respectively. The trial was conducted under randomized block design from $5^{th}$ June to $23^{rd}$ September, 2015. Data were an alyzed through ANOVA technique using SAS9.1.3 software. Results depicted that survivability of soybean against wild birds damage was found better (p<0.05) in thiram coating which was higher than iron coating and control treatment but later on thiram coating had adverse effects on subsequent growth of soybean plants. Corn stalk height was decreased (p<0.05) in thiram coating, whereas corn ear height was reduced in iron coating treatment. Iron coating enhanced (p<0.05) height of soybean plant (p<0.05) better than that of thiram coating. Soybean seed coatings didn't influence dry matter yield and nutritive value in terms of total digestible nutrients yield in corn soybean mixed forage. Conclusively, although presowing thiram coating enhanced survivability of soybean plants against wild bird damage but had adverse effects on its subsequent growth. However, soybean seed coatings didn't influence yield and nutritive value of corn soybean intercropping forage.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.343-348
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• 2015

Soybean is one of the important food crop around the world. Especially in East Asia, it is the main ingredient for traditional food like soy sauce and soy paste. The double cropping system including soybean following onion, Chinese cabbage, and potato is widely adopted in Southern region of Korea. In this system, sowing date of second crop (soybean) can be delayed depending on first crops' growth period and weather condition. When planting date is delayed it is known that soybean yield is declined because of shorter vegetative growth period and earlier flowering induced by warm temperature and changes in photoperiod. The objective of this study was to determine soybean growth and yield responses as plant populations at late planting date. Field experiment was conducted at Department of Functional Crop, National Institute of Crop Science, RDA located in Miryang, Gyeongsangnam-Do for two years ('13-'14) in upland field with mid-late maturity cultivar Daewon. A split-plot block design was used with three replications. Main plots were three sowing dates from June 20 to July 20 with 15 days intervals, and subplots were 4 levels of planting densities. Data of maturity (R8) was recorded, yield components and yield were examined after harvesting. Experimental data were analyzed by using PROC GLM, and DMRT were used for mean comparison. Optimum planting population for maximizing soybean yield in late planting which compared with standard population. In mid-June planting, higher planting density causes increased plant height and decreased diameter which lead to higher risk of lodging, however, reduced growth period due to late planting alleviated this problem. Therefore higher seeding rates can provide protection against low seedling emergence caused by late planting in this region.

• Korean Journal of Microbiology
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• v.54 no.4
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• pp.320-324
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• 2018

Chunkookjang, fermented soybean is rich in diverse oligopeptides which derived from cleavage of soybean proteins during fermentation. Microarray data containing differently expressed genes in breast cancer cells treated with fermented soybean extract and well known breast cancer metastasis markers were combined, and a new network was constructed. It is used to check interactions between the marker proteins and the differently expressed genes. Based on the network analysis, PLAU (plasminogen activator, urokinase, uPA) and ERBB2 (epidermal growth factor receptor 2) are chosen as possible metastasis genes. We treated breast cancer MCF7 cells with fermented soybean extract and measured expression levels of PLAU and ERBB2. Fermented soybean extract suppressed PLAU and ERBB2 expressions conspicuously. In the cancer cells treated with fermented soybean extracts, an inflammation marker, NO production was also reduced. It will be interesting to find specific peptides to suppress PLAU and ERBB2 expressions in human breast cancer cells.