• Title/Summary/Keyword: 일사량 데이터 관리

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The Design of Web-based Crop Information System Using Open-Source Framework and Remotely Sensed Data (오픈 소스 프레임워크와 원격 탐측자료를 이용한 웹 기반 작황 정보 시스템 설계)

  • Nguyen, Minh Hieu;Ma, Jong Won;Lee, Kyungdo;Heo, Joon
    • Korean Journal of Remote Sensing
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    • v.33 no.5_2
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    • pp.751-762
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    • 2017
  • A crop information system can provide information regarding crop distribution, crop growth conditions, crop yield in various forms such as monitoring, forecasting, estimation or analysis. This paper presents the design and construction of a crop information system based on data collected in Korea, USA, and China. Therein, climate data including temperature, precipitation,solar radiation are used to evaluate the impact on crop growth, NDVI (Normalized Difference Vegetation Index) data is used in crop monitoring, and crop map data is utilized for the management of crop distribution. The system has achieved three prominent results: 1) Providing information with high frequency, 2) Automatically creating the report through the analysis of the data, 3) The users to easily approach the system and retrieve the information.

Development of Device Measuring Real-time Air Flow in Greenhouse (온실 공기유동 계측 시스템 개발)

  • Noh, Jae Seung;Kwon, Jinkyoung;Kim, Yu Yong
    • Journal of Bio-Environment Control
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    • v.27 no.1
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    • pp.20-26
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    • 2018
  • This study was conducted to develop a device for measuring the air flow by space variation through monitoring program, which acquires data by each point from each environmental sensor located in the greenhouse. The distribution of environmental factors(air temperature, humidity, wind speed, etc.) in the greenhouse is arranged at 12 points according to the spatial variation and a large number of measurement points (36 points in total) on the X, Y and Z axes were selected. Considering data loss and various greenhouse conditions, a bit rate was at 125kbit/s at low speed, so that the number of sensors can be expanded to 90 within greenhouse with dimensions of 100m by 100m. Those system programmed using MATLAB and LabVIEW was conducted to measure distributions of the air flow along the greenhouse in real time. It was also visualized interpolated the spatial distribution in the greenhouse. In order to verify the accuracy of CFD modeling and to improve the accuracy, it will compare the environmental variation such as air temperature, humidity, wind speed and $CO_2$ concentration in the greenhouse.

Study on the Prediction of short-term Algal Bloom in Juksan weir Using the Model Tree (모델트리를 활용한 죽산보 단기조류예측에 관한 연구)

  • Lee, Bo-Mi;Yi, Hye-Suk;Chong, Sun-A;Joo, Yong-Eun;Kim, Ho-Joon;Choi, Kwang-Soon
    • Proceedings of the Korea Water Resources Association Conference
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    • 2018.05a
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    • pp.450-450
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    • 2018
  • 최근 기후변화와 수온상승으로 인한 녹조발생이 빈번하게 나타나며, 녹조발생에 관한 관심은 꾸준히 증가하고 있는 추세이다. 본 연구는 효율적인 녹조관리를 위하여 모델트리를 활용하여 클로로필-a 단기조류예측 기법을 개발하였다. 대상지역으로 영산강수계의 죽산보를 선정하였으며, 2013년 1월부터 2016년 12월까지 나주 수질자동측정망의 일 단위자료와 동일기간 광주 기상청의 일별 기상자료를 이용하였다. 상관 분석을 통해 T-N, T-P, N/Pratio와 클로로필-a, 수온, 일사량, 강수량을 독립변수로, 단기(t+1일, t+3일, t+5일, t+7일) 클로로필-a를 종속변수로 선정하여 단기조류예측기법을 개발하였다. 수집한 자료의 데이터세트는 격일 간격으로 Training, Testing 기간으로 구분하여 적용한 결과, 상관계수는 1일 예측 시, Training 기간에 0.89, Testing 기간에 0.91, 3일 예측 시, Training 기간에 0.74, Testing 기간에 0.68, 5일 예측 시, Training 기간에 0.70, Testing 기간에 0.66, 7일 예측 시, Training 기간에 0.63, Testing 기간에 0.62로 나타났다. RMSE(Root Mean Square Error)는 1일 예측 시, Training 기간에 13.96, Testing 기간에 12.22, 3일 예측 시, Training 기간에 20.03, Testing 기간에 22.14, 5일 예측 시, Training 기간에 21.32, Testing 기간에 22.57, 7일 예측 시, Training 기간에 23.52, Testing 기간에 23.45로 나타났다. 예측주기에 따라 모델트리와 회귀식에서 활용한 독립변수는 1일 예측 시, 모델트리는 N/Pratio, 클로로필-a, 회귀식은 클로로필-a로 다르게 나타났다. 반면, 3일, 5일, 7일 예측 시, 모델트리와 회귀식에 활용된 변수는 같게 나타났다. 클로로필-a, 수온, 일사량은 5일 예측 시 활용된 변수로, 3일 예측 시에는 기상항목인 강수량이, 7일 예측 시에는 수질항목인 T-N, N/Pratio가 추가되었다. 특히 1일 예측 시 일 때, 높은 예측정도와 활용된 변수의 수가 적게 나타나는 것을 확인하였으며, 예측기간이 길어질수록 예측의 정확성이 낮아지고, 활용된 변수의 수가 많아지는 것을 확인하였다. 향후 적정한 예측기간을 판단하고 예측가능성을 높이기 위해서는 지속적인 자료취득 및 개선이 필요하며, 이를 바탕으로 적절한 단기조류예측이 가능할 것으로 판단된다.

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A Study for Estimation of High Resolution Temperature Using Satellite Imagery and Machine Learning Models during Heat Waves (위성영상과 머신러닝 모델을 이용한 폭염기간 고해상도 기온 추정 연구)

  • Lee, Dalgeun;Lee, Mi Hee;Kim, Boeun;Yu, Jeonghum;Oh, Yeongju;Park, Jinyi
    • Korean Journal of Remote Sensing
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    • v.36 no.5_4
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    • pp.1179-1194
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    • 2020
  • This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R2 (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R2 = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

Implement of Web-based Remote Monitoring System of Smart Greenhouse (스마트 온실 통합 모니터링 시스템 구축)

  • Dong Eok, Kim;Nou Bog, Park;Sun Jung, Hong;Dong Hyeon, Kang;Young Hoe, Woo;Jong Won, Lee;Yul Kyun, Ahn;Shin Hee, Han
    • Journal of Practical Agriculture & Fisheries Research
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    • v.24 no.4
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    • pp.53-61
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    • 2022
  • Growing agricultural products in greenhouses controlled by creating suitable climatic conditions and root zone of crop has been an important research and application subject. Appropriate environmental conditions in greenhouse are necessary for optimum plant growth improved crop yields. This study aimed to establish web-based remote monitoring system which monitors crops growth environment and status of crop on a real-time basis by applying to greenhouses IT technology connecting greenhouse equipment such as temperature sensors, soil sensors, crop sensors and camera. The measuring items were air temperature, relative humidity, solar radiation, CO2 concentration, EC and pH of nutrient solution, medium temperature, EC of medium, water content of medium, leaf temperature, sap flow, stem diameter, fruit diameter, etc. The developed greenhouse monitoring system was composed of the network system, the data collecting device with sensors, and cameras. Remote monitoring system was implemented in a server/client environment. Information on greenhouse environment and crops is stored in a database. Items on growth and environment is extracted from stored information, could be compared and analyzed. So, A integrated monitoring system for smart greenhouse would be use in application practice and understanding the environment and crop growth for smart greenhouse management. sap flow, stem diameter and pant-water relations

Estimation of Reference Crop Evapotranspiration Using Backpropagation Neural Network Model (역전파 신경망 모델을 이용한 기준 작물 증발산량 산정)

  • Kim, Minyoung;Choi, Yonghun;O'Shaughnessy, Susan;Colaizzi, Paul;Kim, Youngjin;Jeon, Jonggil;Lee, Sangbong
    • Journal of The Korean Society of Agricultural Engineers
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    • v.61 no.6
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    • pp.111-121
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    • 2019
  • Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologic water balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is often calculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, $ET_o$). The Penman-Monteith equation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole $ET_o$ method. However, its accuracy is contingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating $ET_o$ from less meteorological data than required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relative humidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables) and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating $ET_o$. The overall findings of this study indicated that $ET_o$ could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the proper choice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent and independent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.