• Korean Journal of Ecology and Environment
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• v.53 no.3
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• pp.304-310
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• 2020

Smart farm is a high-tech type of plant factory that artificially makes environmental conditions suitable for the growth of plants and manages them to automatically produce the desired plants regardless of seasons or space. This study was conducted by identifying the effects of Hertz and Duty ratio on the photosynthetic rate of ginseng, a medicinal crop, to find the optimal conditions for photosynthetic responses in smart farms. The light sources consisted of a total of 10 chambers using LED system, with 4 R+B(red+blue) mixed lights and 6 R+B+W (red+blue+white) mixed lights. In addition, the Hertz of the R+B mixed light was treated at 20, 60, 180, 540, 1620 and 4860 hz respectively. The R+B+W mixed light was treated with 60, 180, 540, and 1620 hz. Afterwards, experiments were conducted with the duty ratio of 30, 50, and 70%. As a result, the photosynthetic rate of ginseng according to duty ratio and Hertz was the highest at 60 hz when duty ratio was set to 50%. On the other hand, that was the lowest when the duty ratio was 30% at the same 60 hz. In addition, the photosynthetic rates were highest in the R+B mixed light and R+B+W mixed light at 60 hz. Therefore, the condition with the highest photosynthetic rate of ginseng in smart farms is 60 hz when the duty ratio in R+B mixed light is 50%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1305-1310
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• 2019

Modern agriculture is changing from resource-oriented agriculture to technology-oriented agriculture. Agriculture, which combines science and technology, is recognized as a new growth engine, and governments, local governments, research institutes, and industry are working together to develop and disseminate various devices necessary for smart farms to build intelligent smart farms. Recently, research is being conducted to build a more intelligent agricultural environment by building a cloud platform. In this paper, we propose a plan to build an urban energy - independent smart farm that can utilize leisure time and agricultural activities by utilizing the rooftop of a city. Also, by using IT technology, various data of smart farm can be managed on remote server, and HMI module for controlling internal environment of smart farm can be developed to manage smart farm automatically or semi-automatically. The service model suggests a model that can manage the internal environment of the smart farm based on mobile.

• Convergence Security Journal
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• v.22 no.5
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• pp.69-80
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• 2022

Now that climate change and food resource security are becoming issues around the world, smart farms are emerging as an alternative to solve them. In addition, changes in the production environment in the primary industry are a major concern for people engaged in all primary industries (agriculture, livestock, fishery), and the resulting food shortage problem is an important problem that we all need to solve. In order to solve this problem, in the primary industry, efforts are made to solve the food shortage problem through productivity improvement by introducing smart farms using the 4th industrial revolution such as ICT and BT and IoT big data and artificial intelligence technologies. This is done through the public and private sectors.This paper intends to consider the minimum requirements for the smart farm data collection system for the development and utilization of smart farms, the establishment of a sustainable agricultural management system, the sequential system construction method, and the purposeful, efficient and usable data collection system. In particular, we analyze and improve the problems of the data collection system for building a Korean smart farm standard model, which is facing limitations, based on in-depth investigations in the field of livestock and livestock (pig farming) and analysis of various cases, to establish an efficient and usable big data collection system. The goal is to propose a method for collecting big data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.256-264
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• 2023

Smart farm technology using IoT is one of the technologies that can increase productivity and improve the quality of agricultural products in agriculture, which is facing difficulties due to the decline in rural population, lack of rural manpower due to aging, and increase in diseases and pests due to climate change. . Smart farms using existing IoT simply monitor farms, implement smart plant growers, and have automatic greenhouse opening and closing systems. This paper implements a smart farm system based on MQTT, an industry standard protocol for the Internet of Things, and Node-RED, a representative development middleware for the Internet of Things. First, data is extracted from Arduino sensors, and data is collected and transmitted from IoT devices using the MQTT protocol. Then, Node-RED is used to process MQTT messages and store the sensing data in real time in MongoDB, a representative NoSQL, to store the data. Through this smart farm system, farm managers can use a computer or mobile phone to check sensing information on the smart farm in real time, anytime, anywhere, without restrictions on time and space.

• Journal of Korean Society for Quality Management
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• v.49 no.4
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• pp.569-579
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• 2021

Purpose: The purpose of this study is the time series analysis for predicting the yield of crops applicable to each farm using environmental variables measured by smart farms cultivating tomato. In addition, it is intended to confirm the influence of environmental variables using a deep learning model that can be explained to some extent. Methods: A time series analysis was performed to predict production using environmental variables measured at 75 smart farms cultivating tomato in two periods. An LSTM-based encoder-decoder model was used for cases of several farms with similar length. In particular, Dual Attention Mechanism was applied to use environmental variables as exogenous variables and to confirm their influence. Results: As a result of the analysis, Dual Attention LSTM with a window size of 12 weeks showed the best predictive power. It was verified that the environmental variables has a similar effect on prediction through wieghtss extracted from the prediction model, and it was also verified that the previous time point has a greater effect than the time point close to the prediction point. Conclusion: It is expected that it will be possible to attempt various crops as a model that can be explained by supplementing the shortcomings of general deep learning model.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.851-858
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• 2018

In this paper, a temperature and humidity algorithm was proposed to enhance the economic efficiency and productivity of smart farm. The basic conditions of smart farms were analyzed, and the information exchange system between sensors and control objects in smart farms based on wireless communication was designed. Based on this, a temperature and humidity control algorithm was developed so that temperature, humidity and soil humidity within smart farm can be followed in predefined values for plant growth. To verify the validity of the proposed design methodology and control algorithm, a prototype of small scale smart farm based on 2.4GHz wireless communication were built and their validity was confirmed through repeated temperature and humidity test.

• Smart Media Journal
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• v.11 no.5
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• pp.9-16
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• 2022

Recently, agricultural sites are automating into digital agricultural smart farms by applying technologies such as big data and Internet of Things (IoT). These smart farms aim to increase production and improve crop quality by measuring the environment of crops, investigating and processing data. Production prediction is an important study in smart farm digital agriculture, which is a high-tech agriculture, and it is necessary to analyze environmental data using big data and further standardized research to manage the quality of growth information data. In this paper, environmental and production data collected from smart farm strawberry farms were analyzed and studied. Based on regression analysis, crop production prediction models were analyzed using Ridge Regression, LightGBM, and XGBoost. Among the three models, the optimal model was XGBoost, and R2 showed 82.5 percent explanatory power. As a result of the study, the correlation between the amount of positive fluid absorption and environmental data was confirmed, and significant results were obtained for the production prediction study. In the future, it is expected to contribute to the prevention of environmental pollution and reduction of sheep through the management of sheep by studying the amount of sheep absorption, such as information on the growing environment of crops and the ingredients of sheep.

• Journal of Internet of Things and Convergence
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• v.2 no.3
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• pp.31-35
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• 2016

IoT technologies have been rapidly developed in recent years, and applied to many industries. In the field of fisheries, the water quality management system have been developed, helping in improving productivity and working environment. In this paper, we have designed the smart feeding system, interoperable with the water quality system, using LPWA network. LPWA network is an IoT network, which is appropriate to fish farms because of its wide area coverages and low power consumption. We expect this work to contribute to developing the aquaculture technology through the big data analysis with the accumulated data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.932-945
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• 2018

Everything in the world is becoming connected and interactive due to the Internet. The future of interactive smart environments such as smart cities, smart industries, or smart farms demand high network bandwidth, high network flexibility, and self-organization systems without costly hardware upgrades, and they provide a sustainable, scalable, and replicable smart environment backbone infrastructure. This paper presents a new Hybrid Software-Defined architecture for integrating Internet-of-Things technologies that are essential technologies for smart environments. It combines a software-defined networking infrastructure and a real-time distributed network framework with an advanced optimization to enable self-configuration, self-management, and self-adaption for providing seamless communication and efficiently managing a vast number of smart heterogeneous devices.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.145-152
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• 2020

In this paper, we propose the Crop Classification Management System (CCMS) that detects changes in growth environment to improve crop production rate. The CCMS consists of two modules. First, the Crop Classification Module (CCM) classifies crops through CNN. Second, the Farm Anomaly Detection Module (FADM) detects abnormal crops by comparing accumulated data of farms. The CCM recognizes crops currently grown on farms and sends them to the FADM, and the FADM picks up the weather data from the past to the present day of the farm growing the crops and applies them to the Nelson rules. The FADM uses the Nelson rules to find out weather data that has occurred and adjust farm conditions through IoT devices. The performance analysis of CCMS showed that the CCM had a crop classification accuracy of about 90%, and the FADM improved the estimated yield by up to about 30%. In other words, managing farms through the CCMS can help increase the yield of smart farms.