• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.75-75
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• 2022

Traditional agriculture mostly focused on activity in the field, but current agriculture faces problems such as reduction of agricultural inputs, labor shortage and so on. Accordingly, traditional agricultural experiments generally considered the simple treatment effects, but current agricultural experiments need to consider the several and complicate treatment effects. To analyze such several and complicate treatment effects, data collection has the first priority. Remote sensing is a quite effective tool to collect information in agriculture, and recent easier availability of UAVs (Unmanned Aerial Vehicles) enhances the effectiveness. LAI (Leaf Area Index) is one of the most important information for evaluating the condition of crop growth. In this study, we utilized UAV with multispectral camera to evaluate plant-based LAI of sweetcorn in a small-scale field experiment and discussed the feasibility of a new experimental design to analyze the several and complicate treatment effects. The plant-based SR measured by UAV showed the highest correlation coefficient with LAI measured by a canopy analyzer in 2018 and 2019. Application of linear mix model showed that plant-based SR data had higher detection power due to its huge number of data although SR was inferior to evaluate LAI than the canopy analyzer. The distribution of plant-based data also statistically revealed the border effect in treatment plots in the traditional experimental design. These results suggest that remote sensing with UAVs has the advantage even in a small-scale experimental plot and has a possibility to provide a new experimental design if combined with various analytical applications such as plant size, shape, and color.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.4
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• pp.21-30
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• 2022

Renewable energy systems aim to achieve carbon neutrality and replace fossil fuels. Photovoltaic technologies are the most widely used renewable energy. However, they require a large operating area, thereby decreasing available farmland. Accordingly, agrivoltaic systems (AVSs)-innovative smart farm technologies that utilize solar energy for crop growth and electricity production-are attracting attention. Although several empirical studies on these systems have been conducted, comprehensive research on their design is lacking, and no standard model suitable for South Korea has been developed. Therefore, this study created an integral design of AVS reflecting domestic crop cultivation conditions and conducted a structural analysis for safety assessment. The shading ratio, planting distance, and agricultural machinery work of the system were determined. In addition, national construction standards were applied to evaluate their structural safety using a finite element analysis. Through this, the safety of this system was ensured, and structural considerations were put forward. It is expected that the AVS model will allow for a stable utilization of renewable energy and smart farm technologies in rural areas.

• Journal of Advanced Technology Convergence
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• v.1 no.1
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• pp.1-6
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• 2022

Since 2015, the government has been making efforts to distribute Korean smart farms. However, the supply is limited to large-scale facility vegetable farms due to the limitations of technology and current cultivation research data. In addition, the efficiency and reliability compared to the introduction cost are low due to the simple application of IT technology that does not consider the crop growth and cultivation environment. Therefore, in this paper, data analysis services was performed based on public and external data. To this end, a data-based target smart farm system was designed that is suitable for the situation of farms growing in facilities. To this end, a farm risk information notification service was developed. In addition, light environment maps were provided for proper fertilization. Finally, a disease prediction model for each cultivation crop was designed using temperature and humidity information of facility farms. Through this, it was possible to implement a smart farm data service by linking and utilizing existing smart farm sensor data. In addition, economic efficiency and data reliability can be secured for data utilization.

• Journal of Plant Biotechnology
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• v.38 no.4
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• pp.251-257
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• 2011

Rice is the most important crop as a model plant for functional genomics of monocotyledons. Rice is usually transformed using Agrobacterium tumefaciens. However, the transformation efficiency using previous method is still low. In this study, we established a new method by modifying the general Agrobacterium protocol especially in the inoculation and co-cultivation step. We directly inoculated Agrobacterium containing a CIPK15 gene under the control of CaMV 35S promoter and NOS terminator in the pCAM1300 vector into the pre-soaked seeds in N6D media for 24 hours. After 7 days of culture at $25^{\circ}C$, calli were formed on seeds cultured on the co-cultivation medium containing an antioxidant compound (1 mM dithiothreitol) and of Agrobacterium growth-inhibiting agent (3 mg/L silver nitrate). We obtained 35 and 22 transgenic plants in rice cultivars, Gopumbyeo and Ilpumbyeo, with increase of transformation efficiency by 30.4% and 22.6%, respectively compared to the general transformation method. The new method in this study would lead to reduction of substantial labor and time to generate transgenic plants.

• Korean Journal of Environmental Agriculture
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• v.25 no.1
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• pp.40-46
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• 2006

This study was initiated to evaluate the inhibitory effect of selected heavy metals on the growth of Acinetobacter sp. Down as one of the phosphorus accumulating microorganisms (PAO) involved in the enhanced biological phosphorus removal (EBPR) process of the wastewater treatment plant. Acinetobacter sp. was initially selected as a starting model microorganism and was grown under aerobic condition for this experiment. The heavy metals selected and investigated in this study were cadmium (Cd), copper (Cu), mercury (Hg), nickel (Ni), and zinc (Zn). Median $(IC_{50})$ and threshold $(IC_{10})$ inhibitory concentrations for Cd, Cu, Hg, Ni, and Zn were 2.95 and 1.45, 4.92 and 2.53, 0.03 and 0.02, 1.12 and 0.43, 14.84 and 5.46 mg $L^{-1}$, respectively. We demonstrated that most of heavy metals tested in the experiment inhibited the growth of Acinetobacter sp. in the range of predetermined concentrations. Based on the data obtained from the experiment, Hg was the most sensitive to Acinetobacter sp., then Ni, Cd, Cu, and Zn in order.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2002.11a
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• pp.65-68
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• 2002

몇 개 농업관련 시험장에서 북한 벼 품종의 출수반응을 관찰한 결과 북한에서 조생종으로 분류된 품종들은 온도에 민감한 반응을 보여 기준품종인 남한의 오대벼에 비해 출수가 빨랐고, 중생종은 그보다 출수가 늦어 전체적으로 남한 품종의 조만성과 유사하였다 (양 등, 2000). 또한 유전자 분석을 통해 북한품종 및 계통 101개를 남한품종과 비교해 본 결과 40% 유사도 수준에서 남한 품종과 같이 통일형과 자포니까형으로 나눌 수 있었으며, 자포니카형 품종들간 유사도는 80%에 달했다.(정 등, 2001)(중략)

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.68-81
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• 2021

The agricultural ecosystem is one of the major sources of greenhouse gas (GHG) emissions. In order to search for climate change adaptation options which mitigate GHG emissions while maintaining crop yield, it is advantageous to integrate multiple models at a high spatial resolution. The objective of this study was to develop a tool to support integrated assessment of climate change impact b y coupling the DSSAT model and the DNDC model. DNDC Regional Input File Tool(DRIFT) was developed to prepare input data for the regional mode of DNDC model using input data and output data of the DSSAT model. In a case study, GHG emissions under the climate change conditions were simulated using the input data prepared b y the DRIFT. The time to prepare the input data was increased b y increasing the number of grid points. Most of the process took a relatively short time, while it took most of the time to convert the daily flood depth data of the DSSAT model to the flood period of the DNDC model. Still, processing a large amount of data would require a long time, which could be reduced by parallelizing some calculation processes. Expanding the DRIFT to other models would help reduce the time required to prepare input data for the models.

• Journal of Biosystems Engineering
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• v.40 no.4
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• pp.335-344
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• 2015

Purpose: Soybean growth is adversely affected by environmental stresses such as drought, extreme temperatures, and nutrient deficiency. The objective of this study was to develop a method for rapid measurement of drought stress in soybean plants using a hyperspectral fluorescence imaging technique. Methods: Hyperspectral fluorescence images were obtained using UV-A light with 365 nm excitation. Two soybean cultivars under drought stress were analyzed. A partial least square regression (PLSR) model was used to predict drought stress in soybeans. Results: Partial least square (PLS) images were obtained for the two soybean cultivars using the results of the developed model during the period of drought stress treatment. Analysis of the PLS images showed that the accuracy of drought stress discrimination in the two cultivars was 0.973 for an 8-day treatment group and 0.969 for a 6-day treatment group. Conclusions: These results validate the use of hyperspectral fluorescence images for assessing drought stress in soybeans.