• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.10
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• pp.1755-1760
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• 2016

Automation of greenhouses has proved to be extremely helpful in maximizing crop yields and minimizing labor costs. The optimum conditions for cultivating plants are regularly maintained by the use of programmed sensors and actuators with constant monitoring of the system. In this paper, we have designed a prototype of a smart greenhouse using Arduino microcontroller, simple yet improved in feedbacks and algorithms. Only three important microclimatic parameters namely moisture level, temperature and light are taken into consideration for the design of the system. Signals acquired from the sensors are first isolated and filtered to reduce noise before it is processed by Arduino. With the help of LabVIEW program, Time domain analysis and Fast Fourier Transform (FFT) of the acquired signals are done to analyze the waveform. Especially, for smoothing the outlying data digitally, Moving average algorithm is designed. With the implement of this algorithm, variations in the sensed data which could occur from rapidly changing environment or imprecise sensors, could be largely smoothed and stable output could be created. Also, actuators are controlled with constant feedbacks to ensure desired conditions are always met. Lastly, data is constantly acquired by the use of Data Acquisition Hardware and can be viewed through PC or Smart devices for monitoring purposes.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.373-379
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• 2019

Smart-farm has been spreading across Korea to improve the labor efficiency and productivity of greenhouse crops. Although notable improvements have been made in the monitoring technologies and environmental-controlling systems in greenhouses, only a few simple decision-support systems are available for predicting the optimum environmental conditions for crop growth. In this study, a tomato growth model (GreenTom), which was developed by Seoul National University in 1997, was calibrated and validated to examine if the model can be used as a decision-supporting system. The original GreenTom model was not able to simulate artificial defoliation, which resulted in overestimation of the leaf area index in the late growth. Thus, an algorithm for simulating the artificial defoliation was developed and added to the original model. The node development, leaf growth, stem growth, fruit growth, and leaf area index were generally well simulated by the modified model indicating that the model could be used effectively in the decision-making of smart greenhouse.

• Journal of Information Technology Services
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• v.19 no.3
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• pp.139-149
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• 2020

There are now many different commercial weather sensors suitable for smart farms, and various smart farm devices are being developed and distributed by companies participating in the government-led smart farm expansion project. However, most do not comply with standard specifications and are therefore limited to use in smart farms. This paper proposed the connecting structure of operating non-standard node devices in smart farms following standard specifications supporting smart greenhouse. This connecting structure was proposed as both a virtual node module method and a virtual node wrapper method. In addition, the SoftFarm2.0 system was experimentally operated to analyze the performance of the implementation of the two methods. SoftFarm2.0 system complies with the standard specifications and supports non-standard smart farm devices. According to the analysis results, both methods do not significantly affect performance in the operation of the smart farm. Therefore, it would be good to select and implement the method suitable for each non-standard smart farm device considering environmental constraints such as power, space, distance of communication between the gateway and the node of the smart farm, and software openness. This will greatly contribute to the spread of smart farms by maximizing deployment cost savings.

• Smart Structures and Systems
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• v.26 no.1
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• pp.117-134
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• 2020

Over recent years, the interest for vegetables and fruits in all seasons and places has much increased, from where diverse countries have directed to the commercial production in greenhouse. In this article, we propose an algorithm based on wireless sensor network technologies that monitor the microclimate inside a greenhouse and linear equations model for optimization plant production and material cost. Moreover, we also suggest a novel design of an intelligent greenhouse. We validate our algorithms with simulations on a benchmark based on experimental data made at lNRA of Montfavet in France. Finally, we calculate the statistical estimators RMSE, TSSE, MAPE, EF and R². The results obtained are promising, which shows the efficiency of our proposed system.

• 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 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.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.284-291
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• 2018

As environmental pollution has become worse green technologies to replace or reduce consumption of fossil fuel get spotlight from government, industry and academia globally. It is reported that 40% of carbon dioxide emission is caused by electricity power generation. And 37% of end user electricity power is used by residential costumer in US. Smart Grid is considered as one of promising technology to alleviate severe environmental problem. In residential environment, Home Energy Management System (HEMS) can play a key role for green smart home. The HEMS can give several benefits like aslowering electric utility bill, improvement of efficiency of electric power consumption and integration of generator using renewable energy resources. However just limited functions of HEMS can be used for residential customer in real life because of lack of smart function in home appliances and optimal managing software for HEMS. This study provides comprehensive analysis for Home Energy Management System for residential customer. Simple HEMS system with real products on the market are explained and limitation of current HEMS are also discussed.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.440-444
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• 2018

Purpose: Cloud-computing technology has several advantages, including maintenance, management, accessibility, and computing power. A greenhouse-control system utilizing these advantages was developed using a private cloud-computing system. Methods: A private cloud needs a collection of servers and a suite of software tools to monitor and control cloud-computing resources. In this study, a server farm, operated by OpenStack as a cloud platform, was constructed using servers, and other network devices. Results: The greenhouse-control system was developed according to the fundamental cloud service models: infrastructure as a service, platform as a service, and software as a service. This system has four additional advantages - security, control function, public data use, and data exchange. There are several considerations that must be addressed, such as service level agreement, data ownership, security, and the differences between users. Conclusions: When the advantages are utilized and the considerations are addressed, cloud-computing technology will be beneficial for agricultural use.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.415-418
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• 2017

Recently, the demand for energy efficiency improvement technology through the connection of waste heat energy and SmartGrid has been increasing. Thus, investments for the cultivation of high value crops and produce is increasing through research aimed at synthetic technology in real-time utilization of smart farms and waste heat energy with the concept of using container greenhouses and plant factories. In this aspect, we have carried out research on a practical application technology that will help farmers to increase the economic effectiveness of LED based plant factories in terms of energy efficiency. This can provide opportunities to connect with the large scale automated smart farms in the future. In this study, we focused on the economic effectiveness of crop cultivation using cooling technology in a container greenhouse through waste heat energy. Hereafter, in order to further advance the technology of real-time monitoring/control of the absorption chiller which is used through the container greenhouses and waste heat energy by using IOT, we would like to propose research on new ideas of agricultural technology that can maximize the utility of cooling energy by operating a mobile gateway based on Raspberry PI.

• Journal of Bio-Environment Control
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• v.29 no.3
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• pp.209-218
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• 2020

This paper investigated actual crop evapotranspiration (ET_c) of tomato and paprika planted in test beds of the greenhouse. Crop water requirement (CWR) is the amount of water required to compensate ET_c loss from the crop. The main objectives of the study are to assess whether the actual crop watering (ACW) was adequate CWR of tomato and paprika and which amount of ACW should be irrigated to each crop. ET_c was estimated using the Penman-Monteith model (P-M) for each crop. ACW was calculated from the difference of amount of nutrient supply water and amount of nutrient drainage water. ACW and CWR of each crop were determined, compared and assessed. Results indicated CWR-tomato was around 100 to 1,200 ml/day, while CWR-paprika ranged from 100 to 500 ml/day. Comparison of ACW and CWR of each crop found that the difference of ACW and CWR are fluctuated following day of planting (DAP). However, the differences could divide into two phases, first the amount of ACWs of each crop are less than CWR in the initial phase (60 DAP) around 500 ml/day and 91 ml/day, respectively. Then, ACWs of each crop are greater than the CWR after 60 DAP until the end of cultivation approximately 400 ml/day in tomato and 178 ml/day in paprika. ET_c assessment is necessary to correctly quantify crop irrigation water needs and it is an accurate short-term estimation of CWR in greenhouse for optimal irrigation scheduling. Thus, reducing ACW of tomato and paprika in the greenhouse is a recommendation. The amount of ACW of tomato should be applied from 100 to 1,200 ml/day and paprika is 100 to 500 ml/day depend on DAP.