• 한국작물학회지
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• 제60권3호
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• pp.394-400
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• 2015

본 연구는 2배체와 4배체 도라지 단백질 발현지도 제작과 도라지의 생리활성에 관여하는 메카니즘을 분자적 수준에서 해석하기 위한 기초자료를 얻고자 실시하였다. 2배체 및 4배체 도라지의 단백질의 발현 양상은 배수성에 따른 특이성은 관찰 할 수 없었으며, 모두 분자량 15~100 kDa 크기, pH 4.0~8.0의 범위에 분포하는 것으로 나타났다. 동정된 39개의 단백질 중 2배체에 비해 4배체에서 2개의 단백질이 up-regulated 되었고, 37개의 단백질이 down-regulated 되었다. 단백질을 기능별로 분류한 결과, 산화환원효소의 활성(oxidoreductase activity)기능을 갖는 단백질이 23.7%의 비율로 가장 많았고 다음은 nucleotide binding 기능의 단백질이 15.8%의 비율로 높았다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.127-127
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• 2017

Aluminum is the most abundant metallic element in the Earth's crust and considered as the most limiting factor for plant productivity in acidic soils. The inhibition of root growth is recognized as the primary effect of Al toxicity. Seeds of wheat cv. Keumkang (Korean cultivar) were germinated on petridish for 5 days and then transferred hydroponic apparatus which was treated with $0{\mu}M$ $AlCl_3$ (control), $100{\mu}M$ $AlCl_3$ and $150{\mu}M$ $AlCl_3$ for 5 days. The length of roots, shoots and fresh weight of wheat seedlings were decreased under aluminum stress. The concentrations of $K^+$, $Mg^{2+}$ and $Ac^{2+}$ were decreased whereas $Al^{3+}$ and $P_2O_5{^-}$ concentration was increased under aluminum stress. Using confocal microscopy, the fluorescence intensity of aluminum was increased with morin staining. In this study, a proteome analysis was performed to identify proteins, which is responsible to aluminum stress in wheat roots. In 10-day-old seedlings, proteins were extracted from roots and separated by 2-DE, stained by CBB. Using image analysis, a total of 47 differentially expressed protein spots were selected, whereas 19 protein spots were significantly up-regulated such as s-adenosylmethionine, oxalate oxidase, malate dehydrogenase, cysteine synthase, ascorbate peroxidase and 28 protein spots were significantly down-regulated such as heat shock protein 70, o-methytransferase 4, enolase, amylogenin by aluminum stress following protein spots analyzed by LTQ-FTICR mass spectrometry. The results provide the global picture of Al toxicity-induced alterations of protein profiles in wheat roots, and identify the Al toxicity-responsive proteins related to various biological processes that may provide some novel clues about plant Al tolerance.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.352-352
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• 2017

Recently, the South Korean government encourages the cultivation of upland crops in the paddy field to maintain an adequate level of rice production and then to balance the demand and supply of rice. This is mainly because the rice consumption per capita per year has continued to decline from 135 kg in 1979 to 61.9 kg in 2016, although the rice production was relatively stable. As a result, the rice overproduction became a big social problem. As a part of that, various upland crops such as soybean, maize, minor cereals and forage crops are planted in the paddy field 10 years ago. The cultivation of these crops may settle the problem of short supply and mass import of the crops to some extent. However, a systematic remote observation of upland crops in the paddy field is very scarce. This study investigated the cultivation status of upland crops and any changes of crop harvesting in the paddy field by using an unmanned aerial vehicle (UAV). Also, we analyzed the kind of upland crops and cultivation area in the paddy field by utilizing time series observation images. A fixed wing UAV is used for the investigation. This is because it is easy to use the flight operation and to control flight management software, and it can automatically cope with various emergency states such as a strong wind and battery discharge. The material of UAV is expanded polypropylene, which has an advantage of less equipment damage and risk during takeoff and landing. We acquired observed images in Buljeong-myeon, Goesan-gun, Chungcheongbuk-do, South Korea by using fixed wing UAV in 2015 and 2016. The total investigated area reaches 6,045 ha, and among them the agricultural area was 1,377 ha. For the next step, we created an orthoimage from all images taken using Pix 4D mapper program. According to the results of image analyses in 2015, the paddy field covered total 577 ha (75.9%) with crop plant. The cultivation area of beans, ginseng, maize, tobacco and peach was 256 ha (36.6%), 63 ha (9.2%), 37 ha (5.4%), 31 ha (4.5%) and 27 ha (3.8), respectively. And in 2016, the total covered area was 586 ha (77.1%), and it was comprised of 253 ha (35.5%), 88 ha (12.3%), 29 ha (4.1%), 22 ha (3.1%) and 32 ha (4.5%) in the same order. In this study, we focused on identifying the paddy field which was converted to the cultivation of upland crops by using UAV. And, it has been indicated that the cultivation area of rice decreased from 141 ha in 2015 to 127 ha in 2016, although that of ginseng increased by 25 ha. As a result, it is expected that a lot of paddy field could be replaced by high-income crops such as ginseng and fruit tree (peach) instead of relative low-income rice. More specific and widespread research on the remote sensing in the paddy field needs to be done.

• 환경생물
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• 제36권4호
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• pp.538-543
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• 2018

흰가루병은 시설 재배 박과류 작물에 광범위하게 발생하여 큰 경제적 피해를 유발하는 병으로서 흰가루병에 의한 피해를 예방하기 위해 실제 현장에서 사용할 수 있는 효과적인 방제체계의 개발이 필요하다. 본 연구에서는 시설오이 재배지의 흰가루병에 대해 오이흰가루병에 등록된 서로 다른 계통의 4개 약제와 2종의 유기농 자재의 약제 교호살포체계별 방제효과를 조사하였다. Pyraclostrobin EC (Pyr, a.i. 22.9%), Hexaconazole SC (Hex, a.i. 2%), Flutianil EC (Flu, a.i. 5%), Penthiopyrad EC (Pen, a.i. 20%) 등 4종의 화학약제를 단일약제 반복처리 및 조합처리 방식으로 총 3회 처리한 결과 $Pyr{\rightarrow}Flu{\rightarrow}Pen$ 처리구는 87%의 높은 방제가를 나타낸 반면, Pyr 연속 3회 처리구는 32.5%의 낮은 방제가를 나타내어 해당 약제에 대한 저항성이 있음을 확인하였다. $Pyr{\rightarrow}Flu{\rightarrow}Pen$ 처리구와 보호용 살균제인 Iminoctadine-tris-albesilate SC (Imi)를 처리한 구 그리고 유기농 자재 2종의 교호살포 처리구의 방제가를 조사한 결과 $Imi{\rightarrow}Flu{\rightarrow}Pen$ 처리구의 방제가는 89%로 가장 높게 나타났으며, 유기농자재 교차살포 처리구의 방제가는 66.3%로 나타났다. 시험 결과를 바탕으로 $Pyr{\rightarrow}Flu{\rightarrow}Pen$, $Imi{\rightarrow}Flu{\rightarrow}Pen$ 처리구는 시설 오이 재배지 내 발생하는 흰가루병 피해를 최소화할 수 있는 효과적인 약제교호살포체계로 생각된다.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.243-243
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• 2017

Leaf colors of rice can be used to identify stress level due to its adaptation to environmental change. For most leaves green-related colors are sourced from chlorophyll a and b. For most leaves green-related colors are consisted of chlorophyll a and b. Chlorophyll concentration is normally measured using a spectrophotometer in laboratory. In some remote observation fields, it is impossible to collect the leaves, preserve them, and bring them to laboratory to measure their chlorophyll content. The measurement of chlorophyll content is observed through its color. Using CIE chromaticity diagram leaf color information in RGB is transformed into wavelength (in nm). Pheophytin contents were also analyzed in 95% ethanol extracts. In the process of leaf development of rice young seedling, both pigments were compared. Leaf samples from different rice seedling bed is taken, their colors and RGB values are recorded using Photoshop Image Analysis. SPAD-502 values were also measured. The chlorophyll and Pheophytin contents were fully estimated by ${\rightthreetimes}_{avg}$ on CIE chromaticity diagram.

• 대한원격탐사학회지
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• 제28권6호
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• pp.597-609
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• 2012

Leaf area index (LAI) is important in explaining the ability of the crop to intercept solar energy for biomass production and in understanding the impact of crop management practices. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of IKONOS, Landsat TM, and MODIS satellite images using empirical models and demonstrates its use with data collected at Missouri field sites. LAI data were obtained several times during the 2002 growing season at monitoring sites established in two central Missouri experimental fields, one planted to soybean (Glycine max L.) and the other planted to corn (Zea mays L.). Satellite images at varying spatial and spectral resolutions were acquired and the data were extracted to calculate normalized difference vegetation index (NDVI) after geometric and atmospheric correction. Linear, exponential, and expolinear models were developed to relate temporal NDVI to measured LAI data. Models using IKONOS NDVI estimated LAI of both soybean and corn better than those using Landsat TM or MODIS NDVI. Expolinear models provided more accurate results than linear or exponential models.

• 한국토양비료학회지
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• 제46권4호
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• pp.245-252
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• 2013

Leaf area index (LAI) is important in explaining the ability of crops to intercept solar energy for biomass production, amount of plant transpiration, and in understanding the impact of crop management practices on crop growth. This paper describes a procedure for estimating LAI as a function of image-derived vegetation indices from temporal series of RapidEye imagery obtained from 2010 to 2012 using empirical models in a rice plain in Seosan, Chungcheongnam-do. Rice plants were sampled every two weeks to investigate LAI, fresh and dry biomass from late May to early October. RapidEye images were taken from June to September every year and corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). Linear, exponential, and expolinear models were developed to relate temporal satellite NDVIs to measured LAI. The expolinear model provided more accurate results to predict LAI than linear or exponential models based on root mean square error. The LAI distribution was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when RapidEye imagery was applied to expolinear model. The spatial trend of LAI corresponded with the variation in the vegetation growth condition.

• Journal of Microbiology and Biotechnology
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• 제31권10호
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• pp.1373-1382
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• 2021

Plant growth promoting rhizobacteria (PGPR) are a group of bacteria that can increase plant growth; but due to unfavorable environmental conditions, PGPR are biologically unstable and their survival rates in soil are limited. Therefore, the suitable application of PGPR as a plant growth stimulation is one of the significant challenges in agriculture. This study presents an intelligent formulation based on Bacillus velezensis VRU1 encapsulation enriched with nanoparticles that was able to control Rhizoctonia solani on the bean. The spherical structure of the capsule was observed based on the Scanning Electron Microscope image. Results indicated that with increasing gelatin concentration, the swelling ratio and moisture content were increased; and since the highest encapsulation efficiency and bacterial release were observed at a gelatin concentration of 1.5%, this concentration was considered in mixture with alginate for encapsulation. The application of this formulation which is based on encapsulation and nanotechnology appears to be a promising technique to deliver PGPR in soil and is more effective for plants.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 추계학술대회
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• pp.368-370
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• 2016

농작물의 재배 목적이 생산량의 목적에서 품질향상으로 변화되어져 왔다. 실외 환경을 농작물 재배에 대해 영향을 줄이기 위해 농가들이 비닐하우스 재배 방식을 많이 사용하고 있다. 이로 인해 농가 비닐하우스 안에 온도를 수시 조정할 필요가 있다. 지금은 대부분 농가들 여전히 온도계로 온도를 측정하고 있다. 본 논문에서는 이런 문제를 해결하기 위해 자동 온도 조절 환경 구축해서 온도를 실시간으로 측정할 수 있으며 농가 생산효율 향상시키고 불필요한 노동력을 감소시킬 수 있다.

• 드라이브 ㆍ 컨트롤
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• 제20권2호
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.