• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1076-1080
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• 2003

$CO_2$ uptake of vegetation is one of the important variables in order to estimate photosynthetic activity, plant growth and carbon budget estimations. The objective of this research was to develop a new estimation method of $CO_2$ uptake of vegetation based on airborne hyperspectral remote sensing measurements in combination with a photosynthetic rate curve model. In this study, a compact airborne spectrographic imager (CASI) was used to obtain image over a field that had been set up to study the $CO_2$ uptake of corn on August 7, 2002. Also, a field survey was conducted concurrently with the CASI overpass. As a field survey, chlorophyll a content, photosynthetic rate curve, Leaf area, dry biomass and light condition were measured. The developed estimation method for $CO_2$ uptake consists of three major parts: a linear mixture model, an enhanced big leaf model and a photosynthetic rate curve model. The Accuracy of this scheme indicates that $CO_2$ uptake of vegetation could be estimated by using airborne hyperspectral remote sensing data in combination with a physiological model.

• International journal of advanced smart convergence
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• v.8 no.1
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• pp.159-165
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• 2019

'4th Industrial Revolution' is accelerating as a core part of creating new growth engines and enhancing competitiveness of businesses. The fourth industrial revolution means the transformation of society and industries that are brought by IoT (Internet of Things), big data analysis, AI (Artificial Intelligence), and robot technology. Information and Communication Technology (ICT), which is a major factor, is affecting production and manufacturing systems and as ICT technologies become more advanced, intelligent information technology is generally utilized in all areas of society, leading to hyper-connected society where new values are created and developed. ICT technology is not just about connecting devices and systems and making smart, it is about constantly converging and harmonizing new technologies in a number of fields and driving innovation and change. It is no exception to the agro-fisheries trade. In particular, ICT technology is applied to the agricultural sector, reducing labor, providing optimal environment for crops, and increasing productivity. Due to the nature of agriculture, which is a labor-intensive industry, it is predicted that the ripple effects of ICT technologies will become bigger. We are expected to use the Smart Concentration using ICT convergence technology as a useful resource for changing smart farms, and to help develop new service markets.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.146-150
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• 2023

In areas where water is scarce, water management is critical. This has an impact on agriculture, as a significant amount of water is used for that purpose. Electronic measurement equipment are essential for regulating and storing soil data. As a result, research has been conducted to manage water usage in the irrigation process. Many equipment for managing soil fertility systems are extremely expensive, making this type of system unaffordable for small farmers. These soil fertility control systems are simple to implement because to recent improvements in IoT technology. The goal of this project is to develop a new methodology for smart irrigation systems. The parameters required to maintain water amount and quality, soil properties, and weather conditions are determined by this IoT-based Smart irrigation System. The system also assists in sending warning signals to the consumer when an error occurs in determining the percentage of moisture in the soil specified for the crop, as well as an alert message when the fertility of the soil changes, since many workers, particularly in big projects, find it extremely difficult to check the soil on a daily basis and operate agricultural devices such as sprinkler and soil fertilizing devices.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.30-30
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• 2021

우리나라 수리시설물 중 30년 이상 경과된 수리시설물은 전체의 61%를 차지하며, 특히 저수지의 경우 저수지의 약 84% 정도는 50년 이상 된 노후 저수지로 분류되고 있어 지속적인 보수·보강 필요하며 향후 기후변화에 취약할 것으로 예상된다. 이수측면에서 설계기준이 되는 설계한발빈도는 농업용 저수지의 내한능력을 나타내는 것으로 수리시설의 규모를 결정하는 기준이 된다. 국내의 경우 1982년 농지개량사업계획 설계기준 댐편에 한발빈도 10년 기준을 채택하여 사용되고 있으며, 현재 농업용 저수지의 이수안전도는 한발빈도 설계기준을 대신하여 사용하고 있다. 농업용 저수지의 이수안전도는 기존 설계기준에 의한 물수지법에 따른 저수지의 설계빈도로 산정되어 기후 및 영농변화, 용수수요의 변화, 농법의 변화 등 현장의 물관리 여건을 반영하는데 한계가 있다. 실제 저수지의 이수능력은 한발빈도 설계기준으로 대변되는 공급가능량 및 평야부 용배수로의 형상에 따라 농업용수 공급역량이 상이하므로, 평야부를 포함하는 농촌유역, 농촌공간의 이수안전도 개념이 도입되어야 한다. 또한 국가의 유관기관들은 특성 및 용도에 맞는 용수공급 정보를 생산하여 모니터링 자료를 제공하고 있지만, 실제 현장에서 체감하는 물 부족 및 이수관련 문제 해결을 위해 현장기반 데이터 활용이 필요하다. 본 연구에서는 기존 경험에 의한 관행적인 물관리 자료, 저수지 관련 계측 자료, 위성영상 자료, 비정형 미디어 데이터 등 이수 관련 분야의 빅데이터를 통합 구축하여 농촌유역 이수안전도의 개념을 정의하고자 한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.485-488
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• 2016

The level of domestic greenhouse complex environmental control technology is a hardware-oriented automation steps that mechanically control the environments of greenhouse, such as temperature, humidity and $CO_2$ through the technology of cultivation and consulting experts. This automation brings simple effects such as labor saving. However, in order to substantially improve the output and quality of agricultural products, it is essential to track the growth and physiological condition of the plant and accordingly control the environments of greenhouse through a software-based complex environmental control technology for controlling the optimum environment in real time. Therefore, this paper is a part of general methods on the greenhouse complex environmental control technology. and presents a horticulture production forecasting methods using artificial neural networks through the analysis of big data systems of smart farm performed in our country and artificial neural network technology trends.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.119-124
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• 2009

In this study, the characteristics of estimating methodology for RUSLE factors such as soil erodibility factor, slope length-steepness factor, and cover management factor were reviewed and then the relative error according to each methodology was analyzed. RUSLE was applied to experimental watershed for 42 storm events and their results were compared with measured sediment yield to examine the applicability of RUSLE. As a result, this paper found that it should be necessary to consider vegetation effect for forest application of RUSLE as cover management was the most sensitive factor. Also, soil erodbility factor was calculated from data of soil series by National Academy of Agricultural Science caused sediment yield to be overestimated because there were big differences between the soil series and on-site soil texture. The 22.7% of maximum relative error was shown according to selecting the rain energy equation. In addition, it will be necessary to verify the RUSLE factors with more data in order to improve their accuracy.

• Korean Journal of Agricultural Science
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• v.31 no.2
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• pp.135-148
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• 2004

Rice is not only main food but also key farm income source of Korean farmers. In spite of the above facts, rice productivity was decreased on account of drought in every 2 or 3 years interval owing to the vulnerability of irrigation facilities throughout Korea in the past decades. As an context of the first five year economic development plan, all weather farming programme including 4 big river basin comprehensive development projects and large and medium sized irrigation water development projects were carried out successfully. Therefore the area of irrigated paddy were increased from 58% in 1970 to 76.2% in 1999. In the past decades, the Government had invested heavy financial funds to develop irrigation water but as an factor share analysis, the contribution rates of irrigation water and investment for farmland base development project have not been identified yet in national agricultural economic level. It is very scarce to find out the papers concerned to macro-economic factor share analysis or contribution rates of water and investment cost to rice production value in Korea considering the production function of the quantity of irrigation water and investment cost as independent variables. Accordingly this paper covered and aimed at identifying (1) derivation of rice production function with the time serial data from 1965 to 1999 and the contribution rates of irrigation water and total investment cost for farmland base development project. The analytical model of the contribution rates was adapted the famous Cobb-Douglass production function. According to the model analysis, the contribution rate of irrigation water to rice production in Korea was shown 37.8% which was equivalent to 0.28 of the production elasticity of water. The contribution rate of farmland base development project cost was revealed 22% and direct production cost of rice was contributed 60% in the growth of rice production and farm mechanization costs contributed to 18% of it respectively. The two contribution rates comparing with the direct production cost were small but without irrigation water and farmland base development, application of high-pay off inputs and farm mechanization might be impossible. Considering the food security and to cope with the frequent drought, rice farming and investment for the irrigation water development should be continued even in WTO system.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1669-1683
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• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.

• Journal of Bio-Environment Control
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• v.30 no.1
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• pp.19-26
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• 2021

In this study, analysis was performed to identify three greenhouses located in the same area using principal component analysis (PCA) and linear discrimination analysis (LDA). The environmental data in the greenhouse were from 3 farms in the same area, and the values collected at 1 hour intervals for a total of 4 weeks from April 1 to April 28 were used. Before analyzing the data, it was pre-processed to normalize the data, and the analysis was performed by dividing it into 80% of the training data and 20% of the test data. As a result of PCA and LDA analysis, it was found that PCA classification accuracy was 57.51% and LDA classification was 67.06%, indicating that it can be classified by greenhouse. Based on the farmhouse data classified in advance, the data of the new environment can be classified into specific groups to determine the tendency of the data. Such data is judged to be a way to increase the utilization of data by facilitating identification.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.1-10
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• 2005

The purpose of this paper is to conduct an experimental measurement and analysis of cities' morphology. Fractal theory that is an effective tool for evaluating self-similarity and complexity of objects was applied. For the comparative analysis of fractailities and computational verification, two totally different cities in Japan were selected. They are Kitakyushu City, which is a big and fully developed city, and Jinguu Machi of which almost all the area is covered with agricultural land use. After converting vector data to raster data within GIS, fractal dimensions of two cases in Kitakyushu City and one case in Jinguu Machi were calculated. The calculation showed that two parts of Kitakyushu City were already fractal. Jinguu Machi, however, was difficult to find fractality. As a conclusion, fractal was proved to be an useful tool to estimate the shape of cities reflecting their internal spatial structure, that is self-similarity and complexity.