• Journal of Soil and Groundwater Environment
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• v.28 no.5
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• pp.51-58
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• 2023

In this study, an empirical experiment was conducted to assess the feasibility of replacing groundwater with rainwater in melon cultivation using a smart rainwater harvesting system. The rainwater harvesting efficiency was calculated under three different melon cultivation scenarios. After cultivation, the quality of the fruits grown with rainwater and groundwater was compared by examining the weight, degree of sweetness, and flesh hardness of the products. The results revealed that the water quality of the smart rainwater harvesting device was suitable for melon cultivation to provide better hardness and chloride levels than groundwater. It was also estimated that about 40% of the total water demand for full growth of the melon could be supplied by rainwater. The fruit weight and sweetness were equivalent or slightly better for the melons cultivated with rainwater than those cultivated with groundwater. In particular, the flesh hardness was significantly improved by rainwater cultivation. These results collectively suggest that rainwater can be used as a substitute for groundwater to preserve groundwater resources without compromizing the produced fruit quality.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.12
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• pp.863-870
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• 2012

The market trends of automatic meter reading associated with smart water meters were investigated. Also, recent developments and field applications of key technology for automatic meter reading associated with smart water meters were analyzed. Smart water meters have been manufactured mostly in United States and Europe and have been expanded their business to Asia. Integrated water management system combining with the additional functions such as real-time consumption metering, cost notification, water conservation, leak detection, water quality monitoring, and flow control have been operated in automatic meter reading. Both water quality and quantity data measured from smart water meters and sensors were transferred to data concentration units through neighborhood area network, and then were transferred to integrated server through wide area network. The data transfer methods were determined by comprehensively considering urban scale, density of smart water meters, power supply and network topologies. Common data collection methods such as fixed network to data concentation units, vehicles drive by, people walk by, and drone fly by have been applied. The automatic meter reading associated with smart water meters are spread throughout the world, and both water and energy savings result in saving the money and reducing the greenhouse gases emission.

• Korean Journal of Environmental Agriculture
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• v.36 no.3
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• pp.193-200
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• 2017

BACKGROUND: Plant growth under smart greenhouse (that is plant factory system) conditions of an artificial light type is significantly depending on the artificial light sources such as a fluorescent lamps or Light-Emitting Diodes (LEDs) with specific spectral wavelengths regardless of the outside environmental changes. In this experiment, characteristics on the growth and compound synthesis of kale seedlings affected by light qualities and intensities provided by LEDs were mentioned. METHODS AND RESULTS: The kale seedlings which developed 3~4 true leaves were exposed by fluorescent lamps or LEDs lights of red (R), blue+white (BW), blue+red (BR) with 50 (L) or $100(H){\mu}mol/m^2/s^1$ photosynthetic photon flux (PPF) under hydroponic culture system of deep flow technique for 50 days. Shoot fresh weight increased under the RH, BWH, and BRH treatments with higher PPF. Shoot elongation of the seedlings decreased, and polyphenol synthesis promoted by the higher light intensity conditions. Sugar synthesis in the leaves was above 2 times greater under the RH treatment of monochromic red light quality with $100{\mu}mol/m^2/s^1\;PPF$ than $50{\mu}mol/m^2/s^1\;PPF$. CONCLUSION: The results show that the control of light quality and intensity in the smart greenhouse conditions with artificial lights significantly affects the growth and compound synthesis in the fresh kale leaves with higher culture efficiency compared to the conventional soil culture under greenhouse or field conditions. Researches on the optimum light intensities of the LEDs with special spectral wavelengths are necessary for maximum growth and metabolism in the seedlings.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.59-64
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• 2020

In the Paris Climate Agreement, Korea submitted an ambitious goal of reducing the greenhouse gas emission forecast (BAU) by 37% by 2030. And as one of the countermeasures, a smart grid, an intelligent power grid, was presented. In order to apply the smart grid, EMS(Energy Management System) needs to be installed and operated in various fields, and the supply is delayed due to the lack of awareness of users and the limitations of system ROI. Therefore, recently, various data analysis and control technologies have been proposed to increase the efficiency of the installed EMS. In this study, we present a measurement control algorithm that analyzes and predicts big data collected by IoT using a SARIMA model to check and operate energy consumption of public sports facilities.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.251-259
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• 2018

BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• Journal of Bio-Environment Control
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• v.24 no.4
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• pp.308-316
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• 2015

This study was conducted to investigate the situation of ICT apply to plastic greenhouse, and the results be apply to design of new one. A CFD analysis were conducted to monitering the ventilation and energy saving of the single span greenhouse newly designed. The causes of delay to apply ICT to plastic greenhouse are the high cost for installation(24%), insufficiency of after services(19%), often disorder(16%), unskillful management of soft ware(15%), insufficient ICT efficiency(13%) and unsatisfying of income increase(12%). The parts of problem occurred in ICT plastic greenhouse are the structure, actuator, environmental control system and sensor(approximate 14%, respectively), remote control technique(13%), plant management technique(12%), energy saving technique(10%) and utilization of software(8%). In the condition of lateral window closed, the average wind speed changed to slow, but it was faster in the condition of leeward side window opened than in the condition of lee and winward side window opened. The air movement in the condition of lateral window closed occur by air moving fan not by out air. It is not affect the room temperature but effective the uniformity of room temperature. The average temperature of low height greenhouse was uniform than high height one. The average temperature in condition of 3rd curtain opened become same with outside temperature after 2 hours, but take more 5 hours in condition of 3rd curtain closed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1254-1262
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• 2021

Recently, the agricultural environment in Korea is rapidly changing due to the aging and decline of the agricultural population, and in order to solve these problems, it is urgently required to improve the agricultural productivity and reduce the labor force. To solve this problem, a smart farm fused with ICT technology is being proposed as an alternative. In Korea, smart farms are currently mainly used in greenhouses. In this paper, this smart farm technology is to be applied to the cultivation of sprouted ginseng. To this end, we use seedlings (about 1.0g) to grow a solid medium and bed for cultivating sprouted ginseng, a fresh ginseng that is produced in a short period of time (2~3 months) with a clean environment management technology that does not use chemical pesticides and hydroponics in a greenhouse developed. In addition, an IoT-based growth environment management system was developed to monitor the growth process of sprouted ginseng in such an environment and to control driving devices.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.51-63
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• 2022

Up-to-date statistics of crop cultivation status is essential for farm land management planning and the advancement in remote sensing technology allows for rapid update of farming information. The objective of this study was to develop a classification model of rice paddy or winter crop fields based on NDWI, NDVI, and HSV indices using Sentinel-2 satellite images. The 18 locations in central Korea were selected as target areas and photographed once for each during summer and winter with a eBee drone to identify ground truth crop cultivation. The NDWI was used to classify summer paddy fields, while the NDVI and HSV were used and compared in identification of winter crop cultivation areas. The summer paddy field classification with the criteria of -0.195

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.47-56
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• 2023

Hydrogen energy is an eco-friendly energy that produces heat and electricity with high energy efficiency and does not emit harmful substances such as greenhouse gases and fine dust. In particular, smart hydrogen energy is an economical, sustainable, and safe future smart hydrogen energy service, which means a service that stably operates based on 'data' by digitally integrating hydrogen energy infrastructure. In this paper, in order to implement a data-based hydrogen charging station demand forecasting model, three hydrogen charging stations (Chuncheon, Sokcho, Pyeongchang) installed in Gangwon-do were selected, supply and demand data of hydrogen charging stations were secured, and 7 machine learning and deep learning algorithms were used. was selected to learn a model with a total of 27 types of input data (weather data + demand for hydrogen charging stations), and the model was evaluated with root mean square error (RMSE). Through this, this paper proposes a machine learning-based hydrogen charging station energy demand prediction model for optimal hydrogen energy supply and demand.