• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.55-62
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• 2024

In this paper, we propose a thin-film hydroponic plant cultivator using HMI display and IoT technology. Existing plant cultivators were difficult to manage due to soil-based cultivation, and it was difficult to optimize environmental conditions due to the open cultivation environment. In addition, there are problems with plant cultivation as immediate control is difficult and growth of plants is delayed. To solve this problem, a cultivation environment was established by connecting the MCU and sensors, and the environment information could be checked and quickly controlled by linking with the HMI display. Additionally, a case was applied to minimize changes in environmental information. Implementation of a thin-film hydroponic cultivation system made soil management easier, improved functionality through operation and control, and made it easy to understand environmental information through the display. The effectiveness of rapid growth was confirmed through crop cultivation experiments in existing growers and hydroponic growers. Future research directions will include optimizing growth information by transmitting and storing cultivation environment information and linking and comparing growth information using vision cameras. It is expected that this will enable efficient and stable plant cultivation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.864-866
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• 2014

현대 사회는 모든 분야에서 다양한 분야들의 기술들이 융합되는 사회이다. 농업 분야 또한 다양한 기술들과의 융합으로 생산 관리와 생육관리 등에서도 변화가 요구되고 있다. 하지만 급속히 정보화가 추진되고 있는 다른 분야에 비해 농업분야는 생물을 다루는 분야이기 때문에 발전 속도가 늦다. 하지만 정보화는 어쩔 수 없는 당면 과제이기에 농업 종합 관리 시스템은 꼭 필요한 시스템이다. 현재의 농업 정보화 시스템은 농업관측정보시스템과 농산물 유통 종합정보 시스템 그리고 농축산물 생산 및 수급정보 분석 시스템 등으로 볼 수 있다. 하지만 이러한 시스템은 초기의 기대와는 달리 농업관측 모형이 현실을 제대로 반영하지 못하는 문제점 등이 있어 비효율적으로 운용되고 있다. 이에 본 연구에서는 식물의 다양한 생육과 생산 최적화를 위해 RFID 센서를 이용하여, 관리함으로서 적은 노동력과 시간의 소모를 줄이고 소비자가 작물에 대한 다양한 정보를 접할 수 있는 시스템을 연구한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.1054-1056
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• 2011

플라스틱 시설원예는 자본과 시설이 요구되지만 제철이 아닐 때도 생산을 통해 생산자의 소득 증대에 기여하고 이는 생산자가 보다 높은 품질의 작물을 생산하는 유인이 된다. 이를 위해서는 재배되는 작물에 최적화된 생육환경을 제공해줄 필요가 있으며 현재까지의 생장데이터를 이용하여 미래의 생장상태를 예측하고, 부족한 부분을 보완해줄 필요가 있다. 본 논문에서는 토마토를 대상으로 플라스틱 시설원예 환경에서의 예측시스템을 설계한다. 동일한 토마토이지만 품종에 따라 생육환경이나 예측모델이 달라질 수 있으므로 다양한 예측모델이 필요에 따라 로딩되어 사용될 수 있도록 한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.3
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• pp.130-137
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• 2023

Smart farms, which have been receiving attention as a solution to recent rural problems, refer to technologies that optimize the growing environment of crops and increase the productivity and quality of crops through efficient management. If the relationships between environmental data in smart farms are analyzed, additional productivity enhancement and crop management will be possible. In this paper, we propose a method for acquiring and analyzing nine environmental data, including temperature, humidity, CO2, soil temperature, soil moisture, insolation, soil EC, EC, and pH. Data acquisition is done through RS-485 communication between the main board and the sensor board and stored in the database after acquisition. The stored data is downloaded in Excel sheet format and analyzed through histograms, data charts, and correlation heatmaps. First, we analyze the distribution of total, day, and night data through histogram analysis, and identifiy the average, median, minimum, and maximum values by month through data chart analysis separating day and night to see how the data changes by month. Finally, we analyze the correlation of the data through a correlation heatmap analysis separating day and night. The results show a very strong positive correlation between temperature and soil temperature and soil EC and EC during the day, and a very strong positive correlation between temperature and soil temperature and soil EC and EC at night, and a strong negative correlation between temperature and soil EC.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.187-195
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• 2019

The needs for precise diagnostics and farm management-decision aids have increased to reduce the risk of climate change and environmental stress. Crop simulation models have been widely used to search optimal solutions for effective cultural practices. However, limited knowledge on physiological responses to environmental variation would make it challenging to apply crop simulation models to a wide range of studies. Advanced research facilities would help investigation of plant response to the environment. In the present study, the sunlit controlled environment chambers, known as Korean SPAR (Soil-Plant-Atmosphere-Research) system, was developed by renovating existing SPAR system. The Korean SPAR system controls and monitors major environmental variables including atmospheric carbon dioxide concentration, temperature and soil moisture. Furthermore, plants are allowed to grow under natural sunlight. Key physiological and physical data such as canopy photosynthesis and respiration, canopy water and nutrient use over the whole growth period are also collected automatically. As a case study, it was shown that the Korean SPAR system would be useful for collection of data needed for understanding the growth and developmental processes of a crop, e.g., soybean. In addition, we have demonstrated that the canopy photosynthetic data of the Korean SPAR indicate the precise representation of physiological responses to environment variation. As a result, physical and physiological data obtained from the Korean SPAR are expected to be useful for development of an advanced crop simulation model minimizing errors and confounding factors that usually occur in field experiments.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.861-866
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• 2023

Unlike conventional smart farms that require chemical fertilizers and large spaces, aquaponics farming, which utilizes the symbiotic relationship between aquatic organisms and crops to grow crops even in abnormal environments such as environmental pollution and climate change, is being actively researched. Different crops require different environments and nutrients for growth, so it is necessary to configure the ratio of aquatic organisms optimized for crop growth. This study proposes a method to measure the degree of growth based on area and volume using image processing techniques in an aquaponics environment. Tilapia, carp, catfish, and lettuce crops, which are aquatic organisms that produce organic matter through excrement, were tested in an aquaponics environment. Through 2D and 3D image analysis of lettuce and real-time data analysis, the growth degree was evaluated using the area and volume information of lettuce. The results of the experiment proved that it is possible to manage cultivation by utilizing the area and volume information of lettuce. It is expected that it will be possible to provide production prediction services to farmers by utilizing aquatic life and growth information. It will also be a starting point for solving problems in the changing agricultural environment.

• Proceedings of the Korean Society of Crop Science Conference
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• 2023.04a
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• pp.7-7
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• 2023

Currently, the main technologies of various fourth industries are big data, the Internet of Things, artificial intelligence, blockchain, mixed reality (MR), and drones. In particular, "digital twin," which has recently become a global technological trend, is a concept of a virtual model that is expressed equally in physical objects and computers. By creating and simulating a Digital twin of software-virtualized assets instead of real physical assets, accurate information about the characteristics of real farming (current state, agricultural productivity, agricultural work scenarios, etc.) can be obtained. This study aims to streamline agricultural work through automatic water management, remote growth forecasting, drone control, and pest forecasting through the operation of an integrated control system by constructing digital twin data on the main production area of the nojinot industry and designing and building a smart farm complex. In addition, it aims to distribute digital environmental control agriculture in Korea that can reduce labor and improve crop productivity by minimizing environmental load through the use of appropriate amounts of fertilizers and pesticides through big data analysis. These open-field agricultural technologies can reduce labor through digital farming and cultivation management, optimize water use and prevent soil pollution in preparation for climate change, and quantitative growth management of open-field crops by securing digital data for the national cultivation environment. It is also a way to directly implement carbon-neutral RED++ activities by improving agricultural productivity. The analysis and prediction of growth status through the acquisition of the acquired high-precision and high-definition image-based crop growth data are very effective in digital farming work management. The Southern Crop Department of the National Institute of Food Science conducted research and development on various types of open-field agricultural smart farms such as underground point and underground drainage. In particular, from this year, commercialization is underway in earnest through the establishment of smart farm facilities and technology distribution for agricultural technology complexes across the country. In this study, we would like to describe the case of establishing the agricultural field that combines digital twin technology and open-field agricultural smart farm technology and future utilization plans.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.162-162
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• 2017

구글에서 공개한 Tensorflow를 이용한 여러 학문 분야의 연구가 활발하다. 농업 시설환경을 대상으로 한 빅데이터의 축적이 증가함과 아울러 실효적인 정보 획득을 위한 각종 데이터 분석 및 마이닝 기법에 대한 연구 또한 활발한 상황이다. 한편, 타 분야의 성공적인 심층학습기법 응용사례에 비하여 농업 분야에서의 응용은 초기 성장 단계라 할 수 있다. 이는 농업 현장에서 취득한 정보의 난해성 및 완성도 높은 생육/환경 모델링 정보의 부재로 실효적인 전과정 처리 기술 도출에 소요되는 시간, 비용, 연구 환경이 상대적으로 부족하기 때문일 것이다. 특히, 센서 기반 데이터 취득 기술 증가에 따라 비약적으로 방대해진 수집 데이터를 시간 복잡도가 높은 심층 학습 모델링 연산에 기계적으로 단순 적용할 경우 시간 효율적인 측면에서 성공적인 결과 도출에 애로가 있을 것이다. 매우 높은 시간 복잡도를 해결하기 위하여 제시된 하드웨어 가속 기능의 경우 일부 개발환경에 국한이 되어 있다. 일례로, 구글의 Tensorflow는 오픈소스 기반 병렬 클러스터링 기술인 MPICH를 지원하는 알고리즘을 공개하지 않고 있다. 따라서, 본 연구에서는 심층학습 기법 연구에 있어서, 예상 가능한 다양한 자원을 활용하여 최대한 연산의 결과를 빨리 도출할 수 있는 하드웨어적인 접근 방법을 모색하였다. 호스트에서 수행하는 일방적인 학습 알고리즘과 달리 이기종간 심층 학습이 가능하기 위해선 우선, NFS(Network File System)를 이용하여 데이터 계층이 상호 연결이 되어야 한다. 이를 위해서 고속 네트워크를 기반으로 한 NFS의 이용이 필수적이다. 둘째로 제한된 자원의 한계를 극복하기 위한 메모 공유 라이브러리가 필요하다. 셋째로 이기종간 프로세서에 최적화된 병렬 처리용 컴파일러를 이용해야 한다. 가장 중요한 부분은 이기종간의 처리 능력에 따른 작업을 고르게 분배할 수 있는 작업 스케쥴링이 수행되어야 하며, 이는 처리하고자 하는 데이터의 형태에 따라 매우 가변적이므로 해당 데이터 도메인에 대한 엄밀한 사전 벤치마킹이 수행되어야 한다. 이러한 요구조건을 대부분 충족하는 Open-CL ver1.2(https://www.khronos.org/opencl/)를 이용하였다. 최신의 Open-CL 버전은 2.2이나 본 연구를 위하여 준비한 4가지 이기종 시스템에서 모두 공통적으로 지원하는 버전은 1.2이다. 실험적으로 선정된 4가지 이기종 시스템은 1) Windows 10 Pro, 2) Linux-Ubuntu 16.04.4 LTS-x86_64, 3) MAC OS X 10.11 4) Linux-Ubuntu 16.04.4 LTS-ARM Cortext-A15 이다. 비교 분석을 위하여 NVIDIA 사에서 제공하는 Pascal Titan X 2식을 SLI로 구성한 시스템을 준비하였다. 개별 시스템에서 별도로 컴파일 된 바이너리의 이름을 통일하고, 개별 시스템의 코어수를 동일하게 균등 배분하여 100 Hz의 데이터로 입력이 되는 온도 정보와 조도 정보를 입력으로 하고 이를 습도정보에 Linear Gradient Descent Optimizer를 이용하여 Epoch 10,000회의 학습을 수행하였다. 4종의 이기종에서 총 32개의 코어를 이용한 학습에서 17초 내외로 연산 수행을 마쳤으나, 비교 시스템에서는 11초 내외로 연산을 마치는 결과가 나왔다. 기보유 하드웨어의 적절한 활용이 가능한 심층학습 기법에 대한 연구를 지속할 것이다

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.135-141
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• 2019

In this study, the "Insect Smart Farm Air Conditioning System" is designed and proposed for the control of breeding environment of Protaetia brevitarsis seulensis larvae. The proposed "Insect Smart Farm Air Conditioning System" separates the breeding room from the air conditioning room. It is a system that creates an environment optimized for breeding and distributes it into a breeding room. When controlling the environment through air-conditioning and humidifiers in insect farms, temperature and humidity vary from part of the breeding room to part. The solution to the problem can be suggested as a solution to the difficulty of producing white-spotted flower mounds of uniform size and weight when selling edible insects. By using the "Insect Smart Farm Air Conditioning System," the temperature difference can be reduced by 6℃ and the humidity difference by 24.7% compared to the environmental control of existing insect farms. The temperature and humidity of different parts of the breeding room were improved. Provide the optimal environment of Protaetia brevitarsis seulensis larvae at all times and ensure uniform CO₂ concentration. It can be expected to increase output through annual production and increase income for insect farmers. The proposed "Insecting Smart Farm Air Conditioning System" also controls the set temperature, humidity and CO₂. Environmental control of the breeding of other edible insects and the reproduction of mushrooms that require environmental control in breeding or breeding will also be possible.