• Korean Journal of Breeding Science
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• v.40 no.3
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• pp.215-222
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• 2008

Fifty-two Korean japonica rice cultivars were analyzed for leaf blast resistance and genotyped with 4 STS and 26 SSR markers flanking the specific chromosome sites linked with blast resistance genes. In our analysis of resistance genes in 52 japonica cultivars using STS markers tightly linked to Pib, Pita, Pi5(t) and Pi9(t), the blast nursery reaction of the cultivars possessing the each four major genes were not identical to that of the differential lines. Eight of the 26 SSR markers were associated with resistant phenotypes against the isolates of blast nursery as well as the specific Korean blast isolates, 90-008 (KI-1113), 03-177 (KJ-105). These markers were linked to Pit, Pish, Pib, Pi5(t), Piz, Pia, Pik, Pi18, Pita and Pi25(t) resistance gene loci. Three of the eight SSR markers, MRG5836, RM224 and RM7102 only showed significantly associated with the phenotypes of blast nursery test for two consecutive years. These three SSR markers also could distinguish between resistant and susceptible japonica cultivars. These results demonstrate the usefulness of marker-assisted selection and genotypic monitoring for blast resistance of rice in blast breeding programs.

• 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.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.33-43
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• 2022

South Korea's agricultural nitrogen balance and phosphorus balance rank first and second, respectively, among OECD countries, and proper nutrient management is required to preserve the water quality of rivers and lakes. This study evaluates the effects of furrow mulching on the reduction of non-point source pollution (NPS) load from a sloped upland. The study site was Wanju-gun, Jeollabuk-do, and the survey period was from 2018 to 2019. The slope of the testbed was 13%, and the soil type was sandy loam. The cropping system consisted of maize-autumn Chinese cabbage rotation. The testbed was composed of bare soil (bare), control (Cont.), furrow vegetation mulching (FVM), and furrow nonwoven fabric mulching (FFM) plots. Runoff was collected for each rainfall event with a 1/100 sampler, and the NPS load was calculated by measuring the concentrations of SS, T-N, and T-P. The NPS load was then analyzed for the entire monitoring and crop cultivation periods. During the monitoring period, the effect of reducing the NPS load was 1.5%~44.5% for FVM and 13.1%~55.2% for FFM. During the crop cultivation period, it was 1.2%~80.5% for FVM and 27.0%~65.1% for FFM, indicating that FFM was more effective than FVM. As the NPS load was fairly high during the crop conversion period, an appropriate management method needs to be implemented during this period.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.112-112
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• 2017

Rice blast caused by Magnaporthe oryzae is one of the most serious diseases that affect the quantity and quality of rice production. The use of resistant rice varieties would be the most effective way to control the rice blast. However R gene incorporation into the rice variety takes time and pathogen could overcome the R gene effects after for a while. For monitoring the rice blast resistance gene distribution in Korean varieties, the four major blast resistance genes against M. oryzae were screened in a number of Korean rice varieties using molecular markers. Of the 120 rice varieties tested, 40 were found to contain the Pi-5 gene, 25 for the Pi-9 gene, 79 for Pi-b and 40 for the Pi-ta gene. None of these rice varieties includes tested 4 R genes. 3 R genes combination, Pi-5/Pi-9/Pi-b, Pi-5, Pi-9.Pi-ta, or Pi-9/Pi-b/Pi-ta were found in 12 varieties, the rice blast disease severity were showed as resistant in the rice verities containing Pi-9/Pi-b/Pi-ta R genes combination, respectively. Also pathogenic diversity of M. oryzae isolates collected in the rice field from 2004 to 2015 in rice field in Korea were analyzed using rice blast monogenic lines, each harboring a single blast resistance gene. Compatibility of blast isolates against rice blast monogenic lines carrying the resistance genes Pi5, Pi9, Pib, and Piz showed dynamic changes by year. It indicates that pathogen has high evolutionary potential adapted host resistances to increase fitness and would lead to rice blast resistance bred into the cultivar becoming ineffective eventually.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.980-981
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• 2008

Mechanical spring and hydraulic pressure operated mechanisms are applied in most of today's High Voltage Gas Insulated Switchgear(GIS)s. This paper proposes a new type of operation mechanism for GIS circuit breakers rated at 245kV and 40kA. The Motor-Direct-Drive-Mechanism (MDDM) has many advantages compared to conventional operating mechanisms. It has a very simple structure with only one moving part, low mechanical stress and audible noise. It also allows monitoring, operation speed control and self-diagnosis functions.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.114-116
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• 2007

Mechanical spring and hydraulic pressure operated mechanisms are applied in most of today's High Voltage Gas Insulated Switchgear(GIS)s. This paper proposes a new type of operation mechanism for GIS circuit breakers rated at 245kV and 40kA. The Motor-Direct-Drive-Mechanism (MDDM) has many advantages compared to conventional operating mechanisms. It has a very simple structure with only one moving part, low mechanical stress and audible noise. It also allows monitoring, operation speed control and self-diagnosis functions.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.807-821
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• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.709-720
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• 2018

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index ($E{\times}G$) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.1
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• pp.1-14
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• 2015

Climate change caused by elevated greenhouse gases would affect crop production through different pathways in agricultural ecosystems. Because an agricultural ecosystem has complex interactions between societal and economical environment as well as organisms, climate, and soil, adaptation measures in response to climate change on a specific sector could cause undesirable impacts on other sectors inadvertently. An integrated system, which links individual models for components of agricultural ecosystems, would allow to take into account complex interactions existing in a given agricultural ecosystem under climate change and to derive proper adaptation measures in order to improve crop productivity. Most of models for agricultural ecosystems have been used in a separate sector, e.g., prediction of water resources or crop growth. Few of those models have been desiged to be connected to other models as a module of an integrated system. Threfore, it would be crucial to redesign and to refine individual models that have been used for simulation of individual sectors. To improve models for each sector in terms of accuracy and algorithm, it would also be needed to obtain crop growth data through construction of super-sites and satellite sites for long-term monitoring of agricultural ecosystems. It would be advantageous to design a model in a sector from abstraction and inheritance of a simple model, which would facilitate development of modules compatible to the integrated prediction system. Because agricultural production is influenced by social and economical sectors considerably, construction of an integreated system that simulates agricultural production as well as economical activities including trade and demand is merited for prediction of crop production under climate change.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.