• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.65-71
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• 2021

This study was carried out to investigate the effect of the final irrigation timing (FIT) before packaging for long-term transportation on growth, flowering, and crop quality of Phalaenopsis after simulated dark shipping (SDS). Phalaenopsis Sogo Yukidian 'V3' plants grown in 11 cm-diameter plastic pots filled with potting media (sphagnum moss + bark or only sphagnum moss) were packaged in paper boxes for export at 3.5, 7, 10 days (FIT 3.5, 7,10; Experiment 1) and 4, 6, 8, 10 days (FIT 4, 6, 8, 10; Experiment 2) after the final irrigation and then stored in a growth chamber at 20 ± 1℃ and 70 ± 3% RH created for SDS. After 4 weeks, the plants were taken out and grown in a greenhouse at 23 ± 3℃ and 70 ± 5% RH, and crop characteristics were measured during cultivation. In Experiment 1, the survival rate of FIT 3.5 plants was lower than that of FIT 7 and FIT 10. There was no difference between treatments in days to first flower, the number of florets, and the elongation rate of flower stalks. In Experiment 2, the percentage of rotted leaves was lowest in FIT 6 when before forcing and at 12 weeks after forcing, and that of FIT 8 was similar to FIT 6 when before forcing, but slightly increased after 12 weeks. The percentage of rotted leaves of FIT 10 was highest and that of FIT 4 was also high. There was little difference in flowering characteristics among treatments. In conclusion, the FIT before packaging for long-term (4 weeks) transportation of potted Phalaenopsis 'V3' affected the leaf rot rather than the post-shipping growth and flowering. And it was considered appropriate to set the volumetric water content of the potting media just before packaging to about 30%.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.209-215
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• 2020

A winter forage tetraploid rye (Secale cereale L.) cultivar, 'Daegokgreen', was developed at the Department of Central Area Crop Science, NICS, RDA in 2016. The mutant line 'CG11003-8-B', which was induced from rye cultivar 'Gogu' (diploid) by colchicine treatment, was selected for its excellent agronomic performance and was placed in preliminary yield trials for one year, 2013. The line was designated "Homil59" and was tested for regional yield trials at the four locations in Korea from 2014 to 2016. Finally, the new cultivar was named as the 'Daegokgreen' (grant number 8274). The leaf of cultivar 'Daegokgreen' is wide, long and dark-green color. The cultivar also has a big-size grain with light-brown color. The heading date of cultivar 'Daegokgreen' was April 17 which was 2 days later than that of check cultivar 'Gogu'. The tolerance to cold and wet injury, and resistance to powdery mildew and leaf rust of the new cultivar were similar to those of the check cultivar but the resistance to the lodging of the new cultivar was stronger than that of the check. The average roughage fresh and dry matter yield of the new cultivar after 10 days from heading were 37.0 and 7.7 MT ha-1, respectively, which were similar to those (38.4 and 8.0 MT ha-1) of the check cultivar. The roughage quality of 'Daegokgreen' was higher in crude protein content (8.9%) than that of the check cultivar (7.9%), while was similar to the check in total digestible nutrients (56.9%). This cultivar is recommended for fall sowing forage crops at all of crop cultivation areas in Korea.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.9-16
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• 2022

Rice is one of the most important staple foods in Wnju, Jeonbuk, South Korea. However, rice consumption has dramatically decreased as eating habits have diversified owing to rapid economic growth. Recently, floury endosperm rice varieties have been developed to invigorate the rice processing industry, because dry-milled rice flour is economically and environmentally suitable for massive rice flour distribution. The National Institute of Crop Science has developed 'AromaT', an early-maturing black rice with floury-endosperm, suitable for tea and dry milling. 'AromaT' was derived from a cross between 'Suweon542' as the floury endosperm source and 'Heugjinju' as the black and aromatic source. In this study, 'AromaT' and its parents, 'Suweon542' and 'Heugjinju', were analyzed for agronomic traits, anthocyanin content, and their major physicochemical properties by different planting date. The field experiment was conducted in Wanju, Jeollabuk-do Province, South Korea, in 2019. The transplanting dates were May 30 (ordinary season), June 25 (double-cropping season), and July 10 (late season). The yield performance of brown rice 'AromaT' was 330 kg/10 a in the double-cropping cultivation method and was the highest among the transplanting dates. The floury endosperm of 'AromaT' was derived from 'Suweon542' containing 'flo7', located on chromosome 5 and known to control floury endosperm. With the late planting date, the anthocyanin content of 'AromaT' was 570.5 mg/100 g, much higher than that of 'Heugjinju' (376.3 mg/100 mg). The brown rice of 'AromaT' also exhibited the pop-corn-flavoring 2-acetyl-1-pyrroline, exclusively detected in aroma rice varieties. The average particle sizes of 'AromaT' and 'Suweon542' were 67.12 ㎛ and 70.9 ㎛, respectively, lower than that of 'Heugjinju' (95.5 ㎛) with a black transparent endosperm. The average damaged starch content of 'AromaT' was 8.1%, lower than that of 'Heugjinju' (10.05%) and Suweon542 (9.5%). As a result, 'AromaT' with high anthocyanin content, fine particle size, and low damaged starch content is expected to provide a new rice material in various processing fields.

• Journal of Bio-Environment Control
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• v.31 no.4
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• pp.384-392
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• 2022

The objective of this study was to estimated nitrogen content and chlorophyll using RGB, Hyperspectral sensors to diagnose of nitrogen nutrition in apple tree leaves. Spectral data were acquired through image processing after shooting with high resolution RGB and hyperspectral sensor for two-year-old 'Hongro/M.9' apple. Growth data measured chlorophyll and leaf nitrogen content (LNC) immediately after shooting. The growth model was developed by using regression analysis (simple, multi, partial least squared) with growth data (chlorophyll, LNC) and spectral data (SPAD meter, color vegetation index, wavelength). As a result, chlorophyll and LNC showed a statistically significant difference according to nitrogen fertilizer level regardless of date. Leaf color became pale as the nutrients in the leaf were transferred to the fruit as over time. RGB sensor showed a statistically significant difference at the red wavelength regardless of the date. Also hyperspectral sensor showed a spectral difference depend on nitrogen fertilizer level for non-visible wavelength than visible wavelength at June 10th and July 14th. The estimation model performance of chlorophyll, LNC showed Partial least squared regression using hyperspectral data better than Simple and multiple linear regression using RGB data (Chlorophyll R²: 81%, LNC: 81%). The reason is that hyperspectral sensor has a narrow Full Half at Width Maximum (FWHM) and broad wavelength range (400-1,000 nm), so it is thought that the spectral analysis of crop was possible due to stress cause by nitrogen deficiency. In future study, it is thought that it will contribute to development of high quality and stable fruit production technology by diagnosis model of physiology and pest for all growth stage of tree using hyperspectral imagery.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• The Korea Journal of Herbology
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• v.37 no.5
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• pp.89-96
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• 2022

Objectives : This research was performed to analyze the components in the different parts of Lythrum salicaria L. and to compare which parts of L. salicaria L. are appropriate for food development. Methods : L. salicaria L. was extracted in 20% EtOH at 100 ℃ for 4 hours. Cytotoxicity was investigated in 3T3-L1 cells after treatment of 10-500 ㎍/ml L. salicaria L. for 24 hours. Total polyphenol content (TPC) was estimated using 1 N Folin-ciocateu reagent. 2,2-Diphenyl-1-picryhydrazyl (DPPH) radical scavenging activity was estimated using DPPH reagent and gallic acid. The chemical composition was analyzed by high-performance liquid chromatography (HPLC). 1) Results : The half maximal inhibitory concentration (IC50) in the extracts of the whole plant, aerial parts, and root parts was 350 ㎍/ml, over 500 ㎍/ml, and 150 ㎍/ml, respectively. The TPC in the extracts of the whole plant, aerial parts, and root parts was 527.1 mg/g, 422.6 mg/g, and 781.1 mg/g, respectively. The averages of vitexin contents in the aerial parts, and root parts were 256.7 ± 154.9 ㎍/g and 266.1 ± 63.2 ㎍/g, respectively. The averages of TPC in the leaves, roots, flower stalks and stems were 224.0 ± 53.7 tannin acid (TA) mg/g, 221.8 ± 70.2 TA mg/g, 249.8 ± 34.4 TA mg/g, and 67.7±8.9 TA mg/g, respectively. The averages of DPPH radical scavenging activity in the leaves, roots, flower stalks, and stems were 282.01 ± 43.3 gallic acid equivalent (GAE) 𝜇mole/g, 260.16 ± 44.1 GAE 𝜇mole/g, 288.0 ± 9.3 GAE 𝜇mole/g, and 97.6 ± 10.7 GAE 𝜇mole/g, respectively. Conclusions : There were no significant differences in the content of components or antioxidant activity in the aerial parts compared to those in the whole plant of L. salicaria L. Furthermore, the root parts had low extract yield, cytotoxicity, and quality control problems, therefore our results suggest that the use of the aerial part of L. salicaria L. would be the most appropriate for food development.

• Journal of Food Hygiene and Safety
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• v.37 no.4
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• pp.216-224
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• 2022

This study was aimed at validating the analysis methods for deacetylasperulosidic acid (DAA), total sugar, galacturonic acid, glucose, and galactose, which are the indicator components of fermented Morinda citrifolia polysaccharide extract (Vitalbos). We modified the previously reported methods for validating the analytical methods. The specificity, linearity, precision, accuracy, limit of detection (LOD), and limit of quantification (LOQ) were measured using phenol-sulfuric acid method and high-performance liquid chromatography (HPLC). The retention time and spectrum of the standard solution of Vitalbos coincided, confirming the specificity. The calibration curve correlation coefficient (R²), of five indicator components, ranged from 0.9995-0.9998, indicating excellent linearity of 0.99 or more. The intra-day and inter-day precision range of the assay was 0.14-3.01%, indicating a precision of less than 5%. The recovery rate was in the range of 95.13-105.59%, presenting excellent accuracy. The LOD ranged from 0.39 to 0.84 ㎍/mL and the LOQ ranged from 1.18 to 2.55 ㎍/mL. Therefore, the analytical method was validated for DAA, total sugar, galacturonic acid, glucose, and galactose, in Vitalbos. The indicator component content in Vitalbos was determined using a validated method. The contents of DAA, total sugar, galacturonic acid, glucose, and galactose were 2.31±0.06, 475.92±5.95, 72.83±1.05, 71.63±2.44, and 67.30±2.31 mg/g of dry weight, respectively. These results suggest that the developed analytical method is efficient and could contribute to the quality control of Vitalbos, as a healthy functional food material.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.48-59
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• 2023

Ground-level ozone affects human health and plant growth. Ozone is produced by chemical reactions between oxides of nitrogen (NOx) and volatile organic compounds (VOCs) from anthropogenic and biogenic sources. In this study, two different land cover and emission factor datasets were input to the MEGAN v2.1 emission model to examine how these parameters contribute to the biogenic emissions and ozone production. Four input sensitivity scenarios (A, B, C and D) were generated from land cover and vegetation emission factors combination. The effects of BVOCs emissions by scenario were also investigated. From air quality modeling result using CAMx, maximum 1 hour ozone concentrations were estimated 62 ppb, 60 ppb, 68 ppb, 65 ppb, 55 ppb for scenarios A, B, C, D and E, respectively. For maximum 8 hour ozone concentration, 57 ppb, 56 ppb, 63 ppb, 60 ppb, and 53 ppb were estimated by scenario. The minimum difference by land cover was up to 25 ppb and by emission factor that was up to 35 ppb. From the modeling performance evaluation using ground ozone measurement over the six regions (East Seoul, West Seoul, Incheon, Namyangju, Wonju, and Daegu), the model performed well in terms of the correlation coefficient (0.6 to 0.82). For the 4 urban regions (East Seoul, West Seoul, Incheon, and Namyangju), ozone simulations were not quite sensitive to the change of BVOC emissions. For rural regions (Wonju and Daegu) , however, BVOC emission affected ozone concentration much more than previously mentioned regions, especially in case of scenario C. This implies the importance of biogenic emissions on ozone production over the sub-urban to rural regions.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.