• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.274-276
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• 2003

The traditional crop productivity simulations based on crop models are normally site-specific. To simulate regional crop productivity, the spatial crop model is developed in this study by integrating Geographical Information System (GIS) with Erosion Productivity Impact Calculator (EPIC) model. The integration applied a loose coupling approach. Data are exchanged using ASCII or binary data format between GIS and EPIC model without a common user interface. The spatial EPIC model is conducted to simulate the average corn and wheat productivity of 1980s in North China. The results show that the simulation accuracy of the spatial EPIC model is acceptable. The simulation accuracy can be improved by using the detailed crop management information, such as irrigation, fertilizer and tillage schedule.

• Proceedings of the Korean Society of Crop Science Conference
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• 1998.10a
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• pp.368-390
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• 1998

It is urgent and of great importance for us to integrate and systemize the agricultural Information system available in Korea. In order to modernize the new Korean agricultural information system, which is enable to enhance compatability of domestic agricultural production system to those of abroad, and to be ready for the era of information and communication, every individual effort and progress is to be evaluated and systemized based on an integrated network system. In this study a comprehensive review for the present status of the Korean agricultural information system was made, and to prohibit the progress and development of this sector were the barriers identified. A survey was carried out to assess specific agricultural information demanded by local farmers. For the efficient utilization of the local agricultural information, the role of agricultural colleges was emphasized.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.521-530
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• 2018

This paper proposes "The Agriculture Decision-making System(ADS) based on Deep Learning for improving crop productivity" that collects weather information based on location supporting precision agriculture, predicts current crop condition by using the collected information and real time crop data, and notifies a farmer of the result. The system works as follows. The ICM(Information Collection Module) collects weather information based on location supporting precision agriculture. The DRCM(Deep learning based Risk Calculation Module) predicts whether the C, H, N and moisture content of soil are appropriate to grow specific crops according to current weather. The RNM(Risk Notification Module) notifies a farmer of the prediction result based on the DRCM. The proposed system improves the stability because it reduces the accuracy reduction rate as the amount of data increases and is apply the unsupervised learning to the analysis stage compared to the existing system. As a result, the simulation result shows that the ADS improved the success rate of data analysis by about 6%. And the ADS predicts the current crop growth condition accurately, prevents in advance the crop diseases in various environments, and provides the optimized condition for growing crops.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.6
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• pp.552-562
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• 2004

KISTI(Korea Institute of Science and Technology Information) has developed the ${\ulcorner}$KITTI­ACOMS (KISTI-Article COntribution Management System)${\lrcorner}$ as part of the national project for automating the process of processing academic information by societies, in order to convert journals published by academic societies in Korea into an electronic form and make them accessible on the Internet. This system has been developed in the year 2001 and has since been distributed to societies free of charge. The number of societies requesting the service has risen recently, which prompted us to take more recommendations of the societies that adopt this system into account in expanding and standardizing the area of service being provided by the system. This paper will investigate the functions of KISTI-ACOMS constructed for use in the Korean Society of Crop Science and list the functions and requirements for the next system to enhance the on-line paper management system.

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.08a
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• pp.22-46
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• 2005

Spatial and timely information on crop and filed conditions is one of the most important basics for rational and efficient planning and management in agriculture. Remote sensing, GIS, and modeling are powerful tools for such applications. This paper presents an overview of the state of the art in remote sensing of crop and field conditions with some case studies. It is also shown that a synergistic linkage between process-based models and remote sensing signatures enables us to estimate the multiple crop/ecosystem variables at a dynamic mode. Remotely sensed information can greatly reduce the uncertainty of simulation models by compensating for insufficient availability of data or parameters. This synergistic approach allows the effective use of infrequent and multi-source remote sensing data for estimating important ecosystem variables such as biomass growth and ecosystem $CO_2$ flux. This paper also shows a geo-spatial information system that enables us to integrate, search, extract, process, transform, and calculate any part of the data based on ID#, attributes, and/or by river-basin boundary, administrative boundary, or boundaries of arbitrary shape/size all over Japan. A case study using the system demonstrates that the nitrogen load from fertilizer was closely related to nitrate concentration of groundwater. The combined use of remote sensing, GIS and modeling would have great potential for various agro-ecosystem applications.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.159-162
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• 1998

Land cover and land use classification on a huge scale, e.g. national or continental scale, has become more and more important because environmental researches need land cover: And land use data on such scales. We developed a crop field extraction method, which is one of the steps in our land cover classification system for a huge area. Firstly, a crop field model is defined to characterize "crop field" in terms of NDVI value and textual information Textual information is represented by the density of straight lines which are extracted by wavelet transform. Secondly, candidates of NDVI threshold value are determined by "scale-space filtering" method. The most appropriate threshold value among the candidates is determined by evaluating the line density of the area extracted by the threshold value. Finally, the crop field is extracted by applying level slicing to Landsat TM image with the threshold value determined above. The experiment demonstrates that the extracted area by this method coincides very well with the one extracted by visual interpretation.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.4
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• pp.22-31
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• 2008

Recently, with the develop of the location tracking technology using GPS and mobile device such as PDA and UMPC, it is used in various application fields that is coordinate transformation and compensation using GPS, is client development for Mobile GIS using GIS and is development of surface survey system for archaeological site based on mobile. In this paper, we develop the system that can investigate the growth information and production information of rice in crop of the field using mobile device, show position information of user and rice in digital map using GIS and save the investigated crop information and position information in database in server. The system which is developed in this paper consists of modules that the user management module is able to restrict database approaches in authority by of the user, the crops management module is able to save and search crop information in database in server, the map module is able to show position information of user and crops in digital map, the location information module is able to convert received location information from GPS and the communication module is able to send and receive data between GPS and mobile device and between mobile device and database. Finally, this paper shall contribute to efficient management and investigation of birth information of crop in field.

• Proceedings of the KSRS Conference
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• v.1
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• pp.432-435
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• 2006

Potential application topics of KOMPSAT-3 image in the field of precision agriculture are suggested. The topics can be categorized as fundamental and applied ones that have contents of static and dynamic characteristics respectively. As fundamental topics, precision information of agriculture that is related to farmland and its crop attributes, precision information of rural infrastructure that is related to rural village and its facilities, precision information of stream environment that is related to rural water resources and its facilities, and precision information of eco-environment that is especially related to riparian ecology and environmental status are included. As applied topics, precision rural water resources that has thematic contents of continuous and event-based runoff, spatial and temporal soil moisture and evapotranspiration, precision agricultural watershed environment that has the contents of spatial and temporal soil loss, sediment and pollutants transport, and precision temporal and spatial crop growth that has the contents of temporal crop texture, spectral reflectance, leaf area index, spatial crop protein information.