• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.89-94
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• 2018

Recently, interest and necessity for the insect industry has been increasing. Insects, which are referred to as the second food, are being used in various fields. Interest in mass production of insects has been increasing in various fields, and the need for research on environmental analysis for them is also increasing. In some countries in other developed countries, automated breeding systems have been developed that incorporate IoT. However, it is limited to be applied to many kinds of insects, and the price is so expensive that it has not been utilized in farm households. Therefore, we have carried out a study to make an optimal system that can be used practically in a farmhouse. Protaetia brevitarsis used for medicinal and edible purposes. It automatically checks the moisture of sawdust medium, which has the greatest influence on the environment of the slugs, which is the third larva of the grasshopper, and informs the result through the LED. It is applied to the environment to create an optimal breeding environment. I want to make it.

• Journal of People, Plants, and Environment
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• v.23 no.6
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• pp.655-665
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• 2020

Background and objective: This study was conducted to compare the cultivation environment, growth of cut flowers, and management performance of conventional farms and smart farms growing the standard cut chrysanthemum, 'Jinba'. Methods: Conventional and smart farms were selected, and facility information, cultivation environment, cut flower growth, and management performance were investigated. Results: The conventional and smart farms were located in Muan, Jeollanam-do, and conventional farming involved cultivating with soil culture in a plastic greenhouse, while the smart farm was cultivating with hydroponics in a plastic greenhouse. The conventional farm did not have sensors for environmental measurement such as light intensity and temperature and pH and EC sensors for fertigation, and all systems, including roof window, side window, thermal screen, and shading curtain, were operated manually. On the other hand, the smart farm was equipped with sensors for measuring the environment and nutrient solution, and was automatically controlled. The day and night mean temperatures, relative humidity, and solar radiation in the facilities of the conventional and the smart farm were managed similarly. But in the floral differentiation stage, the floral differentiation was delayed, as the night temperature of conventional farm was managed as low as 17.7℃ which was lower than smart farm. Accordingly, the harvest of cut flowers by the conventional farm was delayed to 35 days later than that of the smart farm. Also, soil moisture and EC of the conventional farm were unnecessarily kept higher than those of the smart farm in the early growth stage, and then were maintained relatively low during the period after floral differentiation, when a lot of water and nutrients were required. Therefore, growth of cut flower, cut flower length, number of leaves, flower diameter, and weight were poorer in the conventional farm than in the smart farm. In terms of management performance, yield and sales price were 10% and 38% higher for the smart farm than for the conventional farm, respectively. Also, the net income was 2,298 thousand won more for the smart farm than for the conventional farm. Conclusion: It was suggested that the improved growth of cut flowers and high management performance of the smart farm were due to precise environment management for growth by the automatic control and sensor.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.2
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• pp.121-132
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• 2016

This study proposes an approach for simulating high spatial resolution satellite images acquired under arbitrary sun-sensor geometry using existing images and 3D (three-dimensional) data. First, satellite images, having significant differences in spectral regions compared with those in the simulated image were transformed to the same spectral regions as those in simulated image by using the UPDM (Universal Pattern Decomposition Method). Simultaneously, shadows cast by buildings or high features under the new sun position were modeled. Then, pixels that changed from shadow into non-shadow areas and vice versa were simulated on the basis of existing images. Finally, buildings that were viewed under the new sensor position were modeled on the basis of open library-based 3D reconstruction program. An experiment was conducted to simulate WV-3 (WorldView-3) images acquired under two different sun-sensor geometries based on a Pleiades 1A image, an additional WV-3 image, a Landsat image, and 3D building models. The results show that the shapes of the buildings were modeled effectively, although some problems were noted in the simulation of pixels changing from shadows cast by buildings into non-shadow. Additionally, the mean reflectance of the simulated image was quite similar to that of actual images in vegetation and water areas. However, significant gaps between the mean reflectance of simulated and actual images in soil and road areas were noted, which could be attributed to differences in the moisture content.

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.2
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• pp.104-115
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• 1989

In an effort to clarify the wetted patterns of sandy loam soil under trickle irrigation conditions, the distance of wetted zone, infiltration capacity and soil wetted patterns, etc. were measured by gypsum block as soil moisture sensor located every 5 cm vertically and horizontaly in the soil bin under the such conditions as a). irrigation rates set to 2, 4, 6, 8 liters per hour b). total amount of water applied fixed to 14.62 liters per soil bin c) the hearing force of soil measured by plate penetrometer ranging from 1.04 to 1.22kg/cm$_2$ The results can be summarized as follows ; 1. The wetted distance in horizontal direction(H), the wetted distance in vertical direction(D), the horizontal infiltration capacity (iH) and the vertical infiltration capacity(in)could by explained as a function of time t. 2. The horizontal wetted distance (H) is explained by an exponetial function H= a$.$ t where b was found ranging from 021 to 026 under surface trickle irrigation, which was considered a lotlower than the classical value of 0.5 and these measurements were indifferent to the increasing irrigation rates. 3. As for the surface trickle irrigation where horizontal infiltration capacity(iH) is explained as iH = A $.$ t h, the coefficient A increases with respect to irrigation rates within the limits of 0.89~1.34. 4. In terms of surface trickle irrigation of the ratio of Dm Which is maximum vertical wetted distance to Hm, which is maximum horizontal wetted distance, found to be within range of 1.0 to 1.21. It was also noted that the value of Dm decreses when irrigation rates increases while the value of Hm changes the opposite direction. 5. The optimum location of sensors from emitter for surface trickle irrigation should he inside of hemisphere whose lateral radius is 28~30cm long and vertical radius is 10~12cm long. The distance between emitters should be within 60cm long. 6. In the study of vertical wetted distance( D) where D= a $.$ tb, the exponential coefficient b ranged from 0.61 to 0.75 in surface trickle irrigation, and from 0A9 to 0.68 for subsurface trickle irrigation. These measurements showed an increasing tendency to with respect to irrigation rates. 7. In case of vertical infiltration capacity( in), where iD= A $.$ t 1-h, the coefficient A for surface trickle irrigation found to be within range of 0.16 to 0.19 and did not show any relationships with varying degree of irrigation rates. However, the coefficient was varying from 0.09 to 0.22 and showed a tendency to increase vis-a-vis irrigation rates for subsurface trickle irrigation, in contrast. 8. In the observation of subsurface trickle irrigation, it was found that Dm/Hm ratio was within 1.52 to 1.91 and showed a decreasing tendency with respect to increasing rates of irrigation. 9. The location of sensors for subsurface trickle irrigation follows same pattern as above, with vertical distance from emitter being 10~17cm long and horizontal 22~25cm long. The location of emitter should be 50 cm. 10.The relationship between VS which is the volume of wetted soil and Q which is the total amount of water when soil is reached field capacity could be explained as VS= 2.914Q0.91and the irrigation rates showed no impacts on the above relationship.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.173-178
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• 2016

Many studies have been carried out on monitoring the target by cooperating a drone with remote sensors recently. This monitoring system uses static sensors to measure environmental data and drones to collect measured data. In geotechnical engineering, inspectors go around measuring the safety of construction site and it is impractical to compose a network among numerous sensors in terms of the cost efficiency. In this paper, we propose a data collection system based on interaction between a drone and a few sensors that are installed around the target structure for geotechnical projects. Through experimental results, we also verify the availability and the time and cost efficiency of the proposed system comparing with using inspectors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.127-132
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• 2019

In this paper, we design and build an information system for the growth management of agricultural products and aquatic products by ubiquitous computing. So that, we installed various sensor systems and developed IP Cam to establish monitoring. It was possible to construct an environmental management system based on IT technology by a database that directly controls and manages the growth conditions of agricultural products and aquatic products by monitoring. We have installed various sensors that can acquire data in real time and IP Cam is designed and manufactured in three parts. It was applied to agricultural and marine products and monitored soil moisture, temperature, and environmental information to establish conditions for growth environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.278-278
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• 2021

가뭄이 장기간 지속되어 농업적 가뭄 상태가 되면 토양의 수분이 마르기 시작하면서, 식생의 생장활동이 방해되고, 이는 식생의 광합성 활동까지 영향을 미친다. 광합성을 통해 대기 중의 이산화탄소가 흡수되고 산소 발생이 증가하는데, 광합성이 활발하지 못하면 상대적으로 대기 중의 이산화탄소 농도가 증가한다. 본 연구에서는 이러한 토양수분, 식생활동과 대기 중 이산화탄소의 농도의 관계를 다중분광센서인 MODerate resolution Imaging Spectroradiometer (MODIS) 산출물을 이용하여 분석하였다. 기존 토양수분의 경우, 마이크로파 센서를 통해 산출된 값을 활용했지만, 이는 상대적으로 공간 해상도가 조악하다는 단점을 갖고 있어서 면적이 작은 연구지역을 분석할 때에는 한계점을 갖고 있다. 이러한 문제를 해결하기 위하여 상대적으로 고해상도인 광학센서를 이용한 토양수분 산정 방법을 적용하였다. 또한, MODIS 총 일차생산량 (Gross Primary Productivity, GPP) 산출물을 이용하여 식생 호흡량과의 관계식을 통해 이산화탄소 플럭스를 계산하였다. 원격탐사 기반의 토양수분, 식생지수, 이산화탄소 플럭스를 한국에서 발생한 가뭄 기간 중, 2014년과 2015년도에 대하여 지점 관측자료인 플럭스 타워에서 제공되는 값과 비교 분석하였다. 분석한 결과 토양수분, 식생 지수, 탄소플럭스는 순차적으로 지연시간을 두고 상관성이 발생함을 확인하였다. 토양수분과 식생 지수 사이에는 1개월, 식생지수와 탄소플럭스는 0.5개월의 지연시간 후에 가장 높은 상관성을 보였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.24-24
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• 2022

토양 수분은 육상 생태계를 지배하는 핵심 변수로 널리 간주되어 왔다. 따라서 토양 수분을 모니터링하고 추정하는 것은 수문, 농업, 생화학적, 및 기후 역학을 평가하는 데 필수적이다. 그러나 최대 토양 접촉을 요구하는 기존의 토양 수분 모니터링 방법은, 토양 교란을 최소화하여 토양의 고유 특성을 보전하지 못하는 한계가 있다. 이 문제를 극복하기 위해 본 연구에서는 비접촉 초음파 시스템을 이용하여 토양 수분을 평가 방법을 개발하였다. 이 시스템은 공기-토양 조인트 절반 공간에서 누설 레일리파(Rayleigh wave)를 측정하도록 설계되었다. 토양 수분의 변화에 대한 누설 레일리파의 측정은 통제된 실험 설계에서 모래, 실트, 점토와 같은 세 가지 토양 유형에서 평가하였다. 본 연구 결과에서 세 가지 토양 사례 모두, 누설 레일리파의 에너지와 토양 수분 사이에 밀접한 관계가 있음을 보였다. 그러나 모래에서 얻은 동적 매개변수의 특성은 실트 및 점토의 특성과 다른 형태를 보였다. 이러한 결과는 미세한 토양 입자와 대조적으로 굵은 토양 입자는 증발 과정에서 감소된 토양 강도로 설명될 수 있다. 관측된 누설 레일리파에서 얻은 동적 매개변수를 기반으로 토양 수분을 평가하기 위해 랜덤 포레스트 모형을 이용하였다. 예측된 토양 수분의 정확도는 모든 데이터 및 토양 유형에 관계없이 높은 정확도를 보였다(R² ≥ 0.98, RMSE ≤ 0.0089 m³ m^-3). 즉, 본 연구에서는 레일리파가 토양 교란 없이 토양 수분 변화를 지속적으로 평가할 수 있는 큰 잠재력을 가지고 있음을 보여주었다.

• Asian Journal of Turfgrass Science
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• v.23 no.1
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• pp.9-22
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• 2009

Due to increasing concerns over issues with both water quantity and quality for turfgrass use, research was conducted to determine the response of five warm-season turfgrasses to deficit irrigation and to gain a better understanding of relative drought tolerance. St. Augustinegrass(Stenotaphrum secundatum [Walt.] Kuntze.) cultivars 'Floratam' and 'Palmetto', 'SeaIsle 1' seashore Paspalum(Paspalum vaginatumSwartz.), 'Empire' zoysiagrass(Zoysia japonica Steud.), and 'Pensacola' bahiagrass(Paspalum notatum Flugge) were established in lysimeters in the University of Florida Envirotron greenhouse facility in Gainesville. Irrigation was applied at100%, 80%, 60%, or 40% of evapotranspiration(ET). Evaluations included: a) shoot quality, leaf rolling, leaf firing; b) leaf relative water content(RWC), soil moisture content, chlorophyll content index(CCI), canopy photosynthesis(PS); c) multispectral reflectance(MSR); d) root distribution; and e) water use efficiency. Grasses irrigated at 100% and 80% of ET had no differences in visual quality, leaf rolling, leaf firing, RWC, CCI, and PS. Grasses irrigated at 60% of ET had higher values in physiological aspects than grasses irrigated at 40% of ET. 'Sealsle 1' and 'Palmetto' had a deeper root system than 'Empire' and 'Pensacola', while 'Floratam' had the least amount of root mass. Photosynthesis was positively correlated with visual assessments such as turf quality, leaf rolling, leaf firing, and sensor-based measurements such as CCI, soil moisture, and MSR. Reducing the amount of applied water by 20% did not reduce turfgrass quality and maintained acceptable physiological functioning.