• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.195-197
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• 2003

Soil moisture is a crucial variable in research works of hydrology, meteorology and plant sciences. Adequate soil moisture is essential for plant growth; excesses and deficits of soil moisture must be considered in agricultural practices. There are already several remote sensing methods used for monitoring soil moisture, such as thermal inertia, vegetation water-supplying index, crop water stress index and multi-factor regression. In this paper, an improved method has been discussed which is based on the thermal inertia. We analyzed the problems of monitoring soil moisture using satellites at first, and then put forward an simplified method which directly uses land surface temperature differences to measure soil moisture. Also we have taken the influence of vegetation into account, and import NDVI into the model. The method was used in the study of soil moisture in Heilongjiang Province, China, and we draw the conclusion by the experiments that the model can evidently increase the precision of monitoring soil moisture.

• Korean Journal of Agricultural Science
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• v.37 no.2
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• pp.177-189
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• 2010

These studies were conducted to analysis for weather reaction on the growth and yield component according to meterological elements used Vector Autoregressive Regression(VAR) Model at Daejeon, Hongseong, Geumsan, Nonsan, and Yesan to core of center to Chungnam area in Rice. Reaction of cultivars according to change of meterological elements for growth and yield component effected on heading time in Gancheokbyeo and Mananbyeo, grain number of a spike in Gancheokbyeo, ratio of ripeness in Gancheokbyeo and Geumobyeo 1, amount of milled rice in Geurubyeo and Ansanbyeo, and 1,000 grains weight in Gancheokbyeo, Dasanbyeo, and Hwajinbyeo. An effect on the growth and yield components of meterological elements were influenced by heading date, 1,000 grain weight and ratio of repening as sunshine hours. The cultivars in sensitive reaction for change of weather condition were classified to 14 varieties including Gerubyeo et al., insensitive cultivars were classified to 66 varieties including Gyehwabyeo et. al.

• Korean Journal of Breeding Science
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• v.42 no.4
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• pp.351-356
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• 2010

Brachypodium distachyon is rapidly emerged in biological study and has been currently used as a model system for genetics and functional studies for crop improvement and biofuel production. Phosphinothricin (PPT) has been widely used as a selectable agent, which raises ammonium content and induces toxicity in non-transformed plant cells. However PPT selection is not much effective on Brachypodium callus consequently reducing transformation efficiency. In order to identify the efficient conditions of PPT selection, calli obtained from mature seeds of Brachypodium (PI 254867) were cultured on the callus inducing medium (CIM) or regeneration medium (ReM) containing serial dilutions of the PPT (0, 2, 5, 10, and 15 mg/l) in dark or light condition. Callus growth and ammonium content of each treatment were measured 2 weeks after the treatment. Although callus growth and ammonium content did not show much difference in CIM, slow callus growth and increased ammonium accumulation were found in ReM. No significant difference of ammonium accumulation in response to PPT was found between dark and light conditions. In order to identify major factors affecting increased ammonium accumulation, callus was cultured on the media in combined with phytohormones (2,4-D or kinetin) and carbon sources (sucrose or maltose) containing with PPT (5 mg/l). The highest ammonium content in callus was found in the kinetin and maltose media.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.4
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• pp.27-42
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• 2017

The global rise in atmospheric $CO_2$ concentration and its associated climate change have significant effects on agricultural productivity and hydrological cycle. For food security and agricultural water resources planning, it is critical to investigate the impact of climate change on changes in agricultural productivity and water consumption. APEX-Paddy model, which is the modified version of APEX (Agricultural Policy/Environmental eXtender) model for paddy ecosystem, was used to evaluate rice productivity and evapotranspiration based on climate change scenario. Two study areas (Gimjae, Icheon) were selected and the input dataset was obtained from the literature. RCP (Representitive Concentration Pathways) based climate change scenarios were provided by KMA (Korean Meteorological Administration). Rice yield data from 1997 to 2015 were used to validate APEX-Paddy model. The effects of climate change were evaluated at a 30-year interval, such as the 1990s (historical, 1976~2005), the 2025s (2011~2040), the 2055s (2041~2070), and the 2085s (2071~2100). Climate change scenarios showed that the overall evapotranspiration in the 2085s reduced from 10.5 % to 16.3 %. The evaporations were reduced from 15.6 % to 21.7 % due to shortend growth period, the transpirations were reduced from 0.0% to 24.2 % due to increased $CO_2$ concentration and shortend growth period. In case of rice yield, in the 2085s were reduced from 6.0% to 25.0 % compared with the ones in the 1990s. The findings of this study would play a significant role as the basics for evaluating the vulnerability of paddy rice productivity and water management plan against climate change.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.202-208
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• 2001

Under the constant daylength of 13 hours and growth temperatures of 15$^{\circ}C$ to 27$^{\circ}C$, the final number of loaves (FNL) on the main culm was constant as 15 regardless of temperature in rice variety 'Kwanganbyeo'. Leaf appearance rate (LAR) increased with rising temperature and decreased with phenological development. Threshold temperature (T$_{o}$) was not constant across growth stages, but increased with phenological development. Effective accumulated temperature (EAT), which is calculated by the summation of values subtracting T0 from daily mean temperature, is closely related with number of leaves appeared (LA). LA was fitted to bilinear, quadratic, power and logistic function of EAT. Among the functions, logistic function had the best fitness of which coefficient of determination was $R^2$=0.995. Therefore, LAR prediction model was established by differentiating this function in terms of time: (equation omitted). where dL/dt is LAR, T$_1$ is daily mean temperature, L is the number of leaves appeared, and a, b, and c are constants that were estimated as 41.8, 1098.38, and -0.9273, respectively. When predictions of LA were made by LAR prediction model using data independent of model establishment, the observed and predicted LA showed good agreement of $R^2$$\geq$0.99.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.261-268
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• 2019

Climate change and drought stress are having profound impacts on crop growth and development by altering crop physiological processes including photosynthetic activity. But finding a rapid, efficient, and non-destructive method for estimating environmental stress responses in the leaf and canopy is still a difficult issue for remote sensing research. We compared the relationships between photochemical reflectance index(PRI) and various optical and experimental indices on soybean drought stress under climate change conditions. Canopy photosynthesis trait, biomass change, chlorophyll fluorescence(Fv/Fm), stomatal conductance showed significant correlations with midday PRI value across the drought stress period under various climate conditions. In high temperature treatment, PRI were more sensitive to enhanced drought stress, demonstrating the negative effect of the high temperature on the drought stress. But high CO₂ concentration alleviated the midday depression of both photosynthesis and PRI. Although air temperature and CO₂ concentration could affect PRI interpretation and assessment of canopy radiation use efficiency(RUE), PRI was significantly correlated with canopy RUE both under climate change and drought stress conditions, indicating the applicability of PRI for tracking the drought stress responses in soybean. However, it is necessary to develop an integrated model for stress diagnosis using PRI at canopy level by minimizing the influence of physical and physiological factors on PRI and incorporating the effects of other vegetation indices.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.62 no.4
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• pp.333-345
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• 2017

The potato tuber is known as a rich source of essential nutrients, used throughout the world. Although potato-breeding programs share some priorities, the major objective is to increase the genetic potential for yield through breeding or to eliminate hazards that reduce yield. Glycoalkaloids, which are considered a serious hazard to human health, accumulate naturally in potatoes during growth, harvesting, transportation, and storage. Here, we used the AMMI (additive main effects and multiplicative interaction) and GGE (Genotype main effect and genotype by environment interaction) biplot model, to evaluate tuber yield stability and glycoalkaloid content in six potato cultivars across three locations during 2012/2013. The environment on tuber yield had the greatest effect and accounted for 33.0% of the total sum squares; genotypes accounted for 3.8% and $G{\times}E$ interaction accounted for 11.1% which is the nest highest contribution. Conversely, the genotype on glycoalkaloid had the greatest effect and accounted for 82.4% of the total sum squares), whereas environment and $G{\times}E$ effects on this trait accounted for only 0.4% and 3.7%, respectively. Furthermore, potato genotype 'Superior', which covers most of the cultivated area, exhibited high yield performance with stability. 'Goun', which showed lower glycoalkaloid content, was the most suitable and desirable genotype. Results showed that, while tuber yield was more affected by the environment, glycoalkaloid content was more dependent on genotype. Further, the use of the AMMI and GGE biplot model generated more interactive visuals, facilitated the identification of superior genotypes, and suggested decisions on a variety of recommendations for specific environments.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1489-1503
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• 2022

Drone and sensor technologies are enabling digitalization of agricultural crop's growth information and accelerating the development of the precision agriculture. These technologies could be able to assess damage of crops when natural disaster occurs, and contribute to the scientification of the crop insurance assessment method, which is being conducted through field survey. This study was aimed to calculate lodged damage rate from the vegetation indices extracted by drone based RGB images for soybean. Support Vector Classifier (SVC) models were considered by adding vegetation indices to the Crop Surface Model (CSM) based lodged damage rate. Visible Atmospherically Resistant Index (VARI) and Green Red Vegetation Index (GRVI) based lodged damage rate classification were shown the highest accuracy score as 0.709 and 0.705 each. As a result of this study, it was confirmed that drone based RGB images can be used as a useful tool for estimating the rate of lodged damage. The result acquired from this study can be used to the satellite imagery like Sentinel-2 and RapidEye when the damages from the natural disasters occurred.

• Korean Journal of Breeding Science
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• v.41 no.3
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• pp.252-260
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• 2009

Rice is the most important cereal crop not only in supplying the basic staple food for more than half of the world's population but also as a model plant for functional genomic studies of monocotyledons. Although rice transformation method using A. tumefaciens has already been widely used to generate transgenic plants, the transformation rate is still low in most Korean elite cultivars. We made several modifications of the standard protocol especially in the co-cultivation step to improve the efficiency of the rice transformation. The co-culture medium was modified by the addition of three antioxidant compounds (10.5 mg/L L-cysteine, 1 mM sodium thiosulfate, 1 mM dithiothreitol) and of Agrobacterium growth-inhibiting agent (5 mg/L silver nitrate). Co-cultivation temperature ($23.5^{\circ}C$ for 1 day, $26.5^{\circ}C$ for 6 days) and duration (7 days) were also changed. The plasmid of pMJC-GB-GUS carrying the GUS reporter gene and the bar gene as the selectable marker was used to evaluate the efficiency of the transformation. After co-cultivation, a high level of GUS gene expression was observed in calli treated with the modified method. It is likely that those newly added compounds helped to minimize the damage due to oxidative bursts during plant cell-Agrobacterium interaction and to prevent necrosis of rice cells. And the transformation rate under the modified method was also remarkably increased approximately 8-fold in Heungnambyeo and 2-fold in Ilmibyeo as compared to the corresponding standard method. Furthermore, we could produce the transgenic plants stably from Ilpumbyeo which is a high-quality rice but its transformation rate is extremely low. Transformation and the copy number of transgenes were confirmed by PCR, bar strip and Southern blot analysis. The improved method would attribute reducing the effort and the time required to produce a large number of transgenic rice plants.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.