• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.5
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• pp.459-465
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• 2022

This paper reports a method to use a wireless sensor network deployed in the field to real-time monitor soil moisture, warning when the moisture level reaches a specific value, and wirelessly controlling an additional device (LED or water supply system, etc.). In addition, we report all processes related to wireless irrigation system, including field deployment of sensors, real-time monitoring using a smartphone, data calibration, and control of additional devices deployed in the field by smartphone. A commercially available open-source Internet of Things (IoT) platform, NodeMCU, was used, which was combined with a 9V battery, LED and soil humidity sensor to be integrated into a portable prototype. The IoT-based soil humidity sensor prototype deployed in the field was installed next to a tree for on-site demonstration for the measurement of soil humidity in real-time for about 30 hours, and the measured data was successfully transmitted to a smartphone via Wifi. The measurement data were automatically transmitted via e-mail in the form of a text file, stored on the web, followed by analyses and calibrations. The user can check the humidity of the soil real-time through a personal smartphone. When the humidity of a soil reached a specific value, an additional device, an LED device, placed in the field was successfully controlled through the smartphone. This LED can be easily replaced by other electronic devices such as water supplies, which can also be controlled by smartphones. These results show that farmers can not only monitor the condition of the field real-time through a sensor monitoring system manufactured simply at a low cost but also control additional devices such as irrigation facilities from a distance, thereby reducing unnecessary energy consumption and helping improve agricultural productivity.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.94-94
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• 2021

현재 전 지구적으로 일어나고 있는 극단적 기후현상과 이로 인한 자연재해의 원인은 복합적이다. 기후변화로 인한 영향과 동시에 도시화 또한 하나의 원인으로 작용하고 있다. 이러한 영향을 완화하기 위한 방안으로 도시의 그린인프라와 저영향개발은 최근 지속가능한 발전을 위해 꼭 필요한 요소로 자리잡고 있다. 그린인프라 유형 중 하나인 옥상녹화는 많은 이점을 제공한다. 도시에 위치한 건물의 상층부, 내부 및 주변 온도을 낮춤으로써 얻을 수 있는 에너지절감 효과와 강우 시 상당량의 빗물을 저류함으로써 기대되는 우수유출 저감효과, 그리고 도시 공간 내 식물의 적극적인 도입으로 인한 이산화탄소 배출 저감 효과 등을 기대할 수 있다. 본 연구에서는 도시지역의 열섬현상완화와 유출저감의 방안 중 하나인 옥상녹화(Green roof)의 효과를 정량적으로 평가하기 위해 콘크리트로 이루어진 동일한 제원의 실험동을 구축하고, 실험동 내외부의 연직방향 온도, 습도, 강우, 풍속, 일사량 등의 기상자료를 측정할 수 있는 센서를 설치하였다. 각 실험동에서 측정된 기상자료를 Flux Profile Method를 적용하여 무강우기간과 강우발생기간 동안의 연직 방향의 현열속, 잠열속, 토양열속(H, LE, G) 을 산정하였다. 에너지 평형에 따라 산정된 각 실험동의 열속과 지표면 복사량 관측자료을 정량적으로 비교하여 적용성을 평가하였다. 실험의 대조군인 일반 코팅재로 마감된 콘크리트 지붕의 무강우 기간 중 최대 현열속 693.82 W/m² 잠열속은 330.15 W/m² 으로 나타났으며, 실험군인 옥상녹화가 조성된 지붕의 최대 현열속 436.27 W/m², 잠열속 949.20 W/m² 으로 나타났으며, 산정치와 관측치 시계열의 NSE는 0.81 으로 Flux Profile Method를 통해 산정된 열속의 정확도는 비교적 높은 것으로 나타났다. 이와 같은 방법으로 옥상녹화의 정량적 평가가 가능해짐으로써 향후 기후변화 대응방안 및 전략 수립 시 옥상녹화의 온도저감효과 분석에 적극 활용할 수 있을 것으로 판단된다.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.6
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• pp.435-441
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• 2007

Ground-based optical sensing over the crop canopy provides information on the mass of plant body which reflects the light, as well as crop nitrogen content which is closely related to the greenness of plant leaves. This method has the merits of being non-destructive real-time based, and thus can be conveniently used for decision making on application of nitrogen fertilizers for crops standing in fields. In the present study relationships among leaf nitrogen content of rice canopy, crop growth status, and Normalized Difference Vegetation Index (NDVI) values were investigated. We measured Green normalized difference vegetation index($gNDVI=({\rho}0.80{\mu}m-{\rho}0.55{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.55{\mu}m)$) and NDVI($({\rho}0.80{\mu}m-{\rho}0.68{\mu}m)/({\rho}0.80{\mu}m+{\rho}0.68{\mu}m)$) were measured by using two different active sensors (Greenseeker, NTech Inc. USA). The study was conducted in the years 2005-06 during the rice growing season at the experimental plots of National Institute of Agricultural Science and Technology located at Suwon, Korea. The experiments carried out with randomized complete block design with the application of four levels of nitrogen fertilizers (0, 70, 100, 130kg N/ha) and same amount of phosphorous and potassium content of the fertilizers. gNDVI and rNDVI increased as growth advanced and reached to maximum values at around early August, G(NDVI) were a decrease in values of observed with the crop maturation. gNDVI values and leaf nitrogen content were highly correlated at early July in 2005 and 2006. On the basis of this finding we attempted to estimate the leaf N contents using gNDVI data obtained in 2005 and 2006. The determination coefficients of the linear model by gNDVI in the years 2005 and 2006 were 0.88 and 0.94, respectively. The measured and estimated leaf N contents using gNDVI values showed good agreement ($R^2=0.86^{***}$). Results from this study show that gNDVI values represent a significant positive correlation with leaf N contents and can be used to estimate leaf N before the panicle formation stage. gNDVI appeared to be a very effective parameter to estimate leaf N content the rice canopy.

• Journal of Bio-Environment Control
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• v.25 no.3
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• pp.218-223
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• 2016

The calculation method of ground heat exchange in greenhouses has different ideas in each design standard, so there is a big difference in each method according to the size of greenhouses, it is necessary to establish a more accurate method that can be applied to the domestic. In order to provide basic data for the formulation of the calculation method of greenhouse heating load, we measured the soil temperature distribution and the soil heat flux in three plastic greenhouses of different size and location during the heating period. And then the calculation methods of ground heat exchange in greenhouses were reviewed. The soil temperature distributions measured in the heating greenhouse were compared with the indoor air temperature, the results showed that soil temperatures were higher than room temperature in the central part of greenhouse, and soil temperatures were lower than room temperature in the side edge of greenhouse. Therefore, it is determined that the ground heat gain in the central part of greenhouse and the perimeter heat loss in the side edge of greenhouse are occurred, there is a difference depending on the size of greenhouse. Introducing the concept of heat loss through the perimeter of building and modified to reflect the size of greenhouse, the calculation method of ground heat exchange in greenhouses is considered appropriate. It was confirmed that the floor heat loss measured by using soil heat flux sensors increased linearly in proportion to the temperature difference between indoor and outdoor. We derived the reference temperature difference which change the direction of ground heat flow and the perimeter heat loss factor from the measured heat flux results. In the heating design of domestic greenhouses, reference temperature differences are proposed to apply $12.5{\sim}15^{\circ}C$ in small greenhouses and around $10^{\circ}C$ in large greenhouses. Perimeter heat loss factors are proposed to apply $2.5{\sim}5.0W{\cdot}m^{-1}{\cdot}K^{-1}$ in small greenhouses and $7.5{\sim}10W{\cdot}m^{-1}{\cdot}K^{-1}$ in large greenhouses as design standard data.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.467-480
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• 2011

Spatial and temporal variabilities of NPP(Net Primary Production) retrieved from two satellite instruments, AVHRR(Advanced Very High Resolution Radiometer, 1981-2000) and MODIS(MODerate-resolution Imaging Spectroradiometer, 2000-2006), were investigated. The range of mean NPP from A VHRR and MODIS were estimated to be 894-1068 $g{\cdot}C/m^2$/yr and 610-694.90 $g{\cdot}C/m^2$/yr, respectively. The discrepancy of NPP between the two instruments is about 325 $g{\cdot}C/m^2$/yr, and MODIS product is generally closer to the ground measurement than AVHRR despite the limitation in direct comparison such as spatial resolution and vegetation classification. The higher NPP values over South Korea are related to the regions with higher biomass (e.g., mountains) and higher annual temperature. The interannual NPP trends from the two satellite products were computed, and both mean annual trends show continuous NPP increase; 2.14 $g{\cdot}C/m^2$/yr from AVHRR(1981-2000) and 6.08 $g{\cdot}C/m^2$/yr from MODIS (2000-2006) over South Korea. Specifically, the higher increasing trends over the Southwestern region are likely due to the increasing productivity of crop fields from sufficient irrigation and fertilizer use. The retrieved NPP shows a closer relationship between monthly temperature and precipitation, which results in maximum correlation during summer monsoons. The difference in the detection wavelength and model schemes during the retrieval can make a significant difference in the satellite products, and a better accuracy in the meterological and land use data and modeling applications will be necessary to improve the satellite-based NPP data.

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.473-493
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• 2012

Malaria infection is sensitively influenced by regional meteorological conditions along with global climate change. Remote sensing techniques have become an important tool for extraction of climatic and environmental factors, including rainfall, temperature, surface water, soil moisture, and land use, which are directly linked to the habitat qualities of malaria mosquitoes. Improvement of sensor fidelity with higher spatial and spectral resolution, new multinational sensor development, and decreased data cost have nurtured diverse remote sensing applications in malaria research. In 1984, eradication of endemic malaria was declared in Korea, but reemergence of malaria was reported in mid-1990s. Considering constant changes in malaria cases since 2000, the epidemiological management of the disease needs careful monitoring. Geographically, northmost counties neighboring North Korea have been ranked high in the number of malaria cases. High infection rates in these areas drew special attention and led to a hypothesis that malaria dispersion in these border counties might be caused by north-origin, malaria-bearing adult mosquitoes. Habitat conditions of malaria mosquitoes are important parameters for prediction of the vector abundance. However, it should be realized that malaria infection and transmission is a complex mechanism, where non-environmental factors, including human behavior, demographic structure, landscape structure, and spatial relationships between human residence and the vector habitats, are also significant considerations in the framework of medical geography.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.672-680
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• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• Journal of Biosystems Engineering
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• v.12 no.1
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• pp.53-62
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• 1987

Automation of agricultural machinery is a high technology needed to increase work capacity and accuracy, to save agricultural resources and energy, to solve labor shortage, and to improve operator's comfort and safety. With the rapid development of electronic industry, automation of agricultural machinery will be progressed fast, and eventually will lead to no-operator machines or agricultural robots. Automation should be promoted step by step without increasing the cost of farming, excluding rural labor forces, decreasing labor volition, and losing human nature. In order to achieve rational automation of agricultural machinery, it is necessary to investigate the characteristics of soils and crops, to develop sensors, controllers and robots with artificial intelligence. It is recommended that the present trends to directly automatize the individual machinery be changed to the development of a harmonious automation system for overall farming.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.468-475
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• 2005

To understand spatiotemporal changes of temperature and humidity in Lentimula edodes cultivation sheds, temperature, relative humidity were measured with HOBO H8 series sensors in log cultivation sheds and sawdust cultivation sheds. The results obtained from October in 2003 to October in 2004 were as follows; 1. Horizontal temperature changes were smaller at center of cultivation shed inside than comer of cultivation shed inside, while relative humidity changes were greater about 3% at center of cultivation shed inside than corner of cultivation shed inside. 2. Vertical temperature changes showed that the temperature was higher at above than at below when the temperature rises, while the temperature was lower at above than at below when the temperature falls. Thus close to soil surface temperature showed a little fluctuation. Vertical relative humidity changes showed that the relative humidity was lower at above than at below when the temperature rises, while the relative humidity was higher at above than at below when the temperature falls. After all temperature and relative humidity was the opposite in cultivation shed. 3. It's showed in log cultivation shed that the minimum temperature was a subzero temperature until the end of April, while the minimum temperature did above zero after the beginning of the May. Besides a winter was the greatest at daily temperature range during the four season, about $30^{\circ}C$. On the other hand the minimum relative humidity was less than 20% at April, May and June but more than 40% after May.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.4
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• pp.211-221
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• 2021

The Rabbit grass(Trifolium Repens, call it 'Clover') is a representative harmful plant of lawn, and it starts growing earlier than lawn, forming a water pipe on top of the lawn and hindering the photosynthesis and growth of the lawn. As a result, in competition between lawn and clover, clover territory spreads, but lawn is damaged and dried up. Damage to the affected lawn area will accelerate during the rainy season as well as during the plant's rear stage, spreading the area where soil is exposed. Therefore, the restoration of damaged lawn is causing psychological stress and a lot of economic burden. The purpose of this study is to distinguish clover which is a representative harmful plant on lawn, to identify the distribution of damaged areas due to the spread of clover, and to review of changes in vegetation before and after the eradication of clover. For this purpose, a time series analysis of three vegetation indices calculated based on images of convergence Drone with RGB(Red Green Blue) and BG-NIR(Near Infra Red)sensors was reviewed to identify the separation between lawn and clover for selective eradication, and the distribution of damaged lawn for recovery plan. In particular, examined timeseries changes in the ecology of clover before and after the weed-whacking by manual and brush cutter. And also, the method of distinguishing lawn from clover was explored during the mid-year period of growth of the two plants. This study shows that the time series analysis of the MGRVI(Modified Green-Red Vegetation Index), NDVI(Normalized Difference Vegetation Index), and MSAVI(Modified Soil Adjusted Vegetation Index) indices of drone-based RGB and BG-NIR images according to the growth characteristics between lawn and clover can confirm the availability of change trends after lawn damage and clover eradication.