• 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.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.285-294
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• 2023

The importance of urban green space creation is increasingly recognized as the most realistic and efficient approach for fine dust mitigation in urban areas. Particularly considering the characteristics of domestic cities, the application of buffer green spaces along roads can maximize the efficiency of fine dust reduction without the need for separate green space creation. Accordingly, this study analyzed the fine dust mitigation effects based on the types of plantings in the central dividers and roadside trees in Jeonju City, Jeollabuk-do. To do this, we controlled various external variables of urban space and considered the planting arrangement types in the central dividers, carrying out the analysis using a CFD simulation. The simulation results confirmed that the central dividers with plantings demonstrated more effective ultrafine dust reduction than those without. Moreover, the arrangement of roadside trees showed a greater ultrafine dust reduction effect when adopting a multilayered structure compared to a single layer. Based on these findings, we concluded that installing both trees and shrubs simultaneously in the central dividers and along roads was effective for ultrafine dust mitigation. On this basis, we quantified the dust reduction effects of plants in urban street environments and proposed planting guidelines for roadside green spaces to improve air quality.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.27-33
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• 2018

This study was performed to review the domestic and international smart farm service model based on the convergence of agriculture and information & communication technology and derived various factors needed to improve the Korean smart greenhouse. Studies on modelling of crop growth environment in domestic smart farms were limited. And it took a lot of time to build research infrastructure. The cloud-based research platform as an alternative is needed. This platform can provide an infrastructure for comprehensive data storage and analysis as it manages the growth model of cloud-based integrated data, growth environment model, actuators control model, and farm management as well as knowledge-based expert systems and farm dashboard. Therefore, the cloud-based research platform can be applied as to quantify the relationships among various factors, such as the growth environment of crops, productivity, and actuators control. In addition, it will enable researchers to analyze quantitatively the growth environment model of crops, plants, and growth by utilizing big data, machine learning, and artificial intelligences.

• Journal of Bio-Environment Control
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• v.27 no.2
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• pp.166-172
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• 2018

This study set out to conduct a field survey with smart greenhouse-based farms in seven types to figure out the actual state of smart greenhouses distributed across the nation before selecting a system to implement an optimal greenhouse environment and doing a research on higher productivity based on data related to crop growth, development, and environment. The findings show that the farms were close to an intelligent or advanced smart farm, given the main purposes of leading cases across the smart farm types found in the field. As for the age of farmers, those who were in their forties and sixties accounted for the biggest percentage, but those who were in their fifties or younger ran 21 farms that accounted for approximately 70.0%. The biggest number of farmers had a cultivation career of ten years or less. As for the greenhouse type, the 1-2W type accounted for 50.0%, and the multispan type accounted for 80.0% at 24 farms. As for crops they cultivated, only three farms cultivated flowers with the remaining farms growing only fruit vegetables, of which the tomato and paprika accounted for approximately 63.6%. As for control systems, approximately 77.4% (24 farms) used a domestic control system. As for the control method of a control system, three farms regulated temperature and humidity only with a control panel with the remaining farms adopting a digital control method to combine a panel with a computer. There were total nine environmental factors to measure and control including temperature. While all the surveyed farms measured temperature, the number of farms installing a ventilation or air flow fan or measuring the concentration of carbon dioxide was relatively small. As for a heating system, 46.7% of the farms used an electric boiler. In addition, hot water boilers, heat pumps, and lamp oil boilers were used. As for investment into a control system, there was a difference in the investment scale among the farms from 10 million won to 100 million won. As for difficulties with greenhouse management, the farmers complained about difficulties with using a smart phone and digital control system due to their old age and the utter absence of education and materials about smart greenhouse management. Those difficulties were followed by high fees paid to a consultant and system malfunction in the order.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.160-160
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• 2017

자동화 기술을 통한 한국형 스마트팜의 발전이 비약적으로 이루어지고 있는 가운데 무인화를 위한 지능적인 스마트 시설환경 관찰 및 분석에 대한 요구가 점점 증가 하고 있다. 스마트 시설환경에서 취득 가능한 시계열 데이터는 온도, 습도, 조도, CO2, 토양 수분, 환기량 등 다양하다. 시스템의 경계가 명확함에도 해당 속성의 특성상 타임도메인과 공간도메인 상에서 정확한 추정 또는 예측이 난해하다. 시설 환경에 접목이 증가하고 있는 지능형 관리 기술 구현을 위해선 시계열 공간 데이터에 대한 신속하고 정확한 정량화 기술이 필수적이라 할 수 있다. 이러한 기술적인 요구사항을 해결하고자 시도되는 다양한 방법 중에서 공간 분해능 향상을 위한 다지점 계측 메트릭스를 실험적으로 구성하였다. $50m{\times}100m$의 단면적인 연동 딸기 온실을 대상으로 $3{\times}3{\times}3$의 3차원 환경 인자 계측 매트릭스를 설치하였다. 1 Hz의 주기로 4가지 환경인자(온도, 습도, 조도, CO2)를 계측하였으며, 계측 하는 시점과 동시에 병렬적으로 공간통계법을 이용하여 미지의 지점에 대한 환경 인자들을 실시간으로 추정하였다. 선행적으로 50 cm 공간 분해능에 대응하기 위하여 Kriging interpolation법을 횡단면에 대하여 분석한 후 다시 종단면에 대하여 분석하였다. 3 Ghz에 해당하는 연산 능력을 보유한 컴퓨터에서 1초 동안 획득한 데이터에 대한 분석을 마치는데 소요되는 시간이 15초 내외로 나타났다. 이는 해당 알고리즘의 매우 높은 시간 복잡도(Order of $O=O^3$)에 기인하는 것으로 다양한 시설 환경의 관리 방법론에 적절히 대응하기에 한계가 있다 할 수 있다. 실시간으로 시간 복잡도가 높은 연산을 수행하기 위한 기술적인 과제를 해결하고자, 근래에 관심이 증가하고 있는 NVIDIA 사에서 제공하는 CUDA 엔진과 Apple사의 제안을 시작으로 하여 공개 소프트웨어 개발 컨소시엄인 크로노스 그룹에서 제공하는 OpenCL 엔진을 비교 분석하였다. CUDA 엔진은 GPU(Graphics Processing Unit)에서 정보 분석 프로그램의 연산 집약적인 부분만을 담당하여 신속한 결과를 산출할 수 있는 라이브러리이며 해당 하드웨어를 구비하였을 때 사용이 가능하다. 반면, OpenCL은 CUDA 엔진이 특정 하드웨어에서 구동이 되는 한계를 극복하고자 하드웨어에 비의존적인 라이브러리를 제공하는 것이 다르며 클러스터링 기술과 연계를 통해 낮은 하드웨어 성능으로 인한 단점을 극복하고자 하였다. 본 연구에서는 CUDA 8.0(https://developer.nvidia.com/cuda-downloads)버전과 Pascal Titan X(NVIDIA, CA, USA)를 사용한 방법과 OpenCL 1.2(https://www.khronos.org/opencl/)버전과 Samsung Exynos5422 칩을 장착한 ODROID-XU4(Hardkernel, AnYang, Korea)를 사용한 방법을 비교 분석하였다. 50 cm의 공간 분해능에 대응하기 위한 4차원 행렬($100{\times}200{\times}5{\times}4$)에 대하여 정수 지수화를 위한 Quantization을 거쳐 CUDA 엔진과 OpenCL 엔진을 적용한 비교한 결과, CUDA 엔진은 1초 내외, OpenCL 엔진의 경우 5초 내외의 연산 속도를 보였다. CUDA 엔진의 경우 비용측면에서 약 10배, 전력 소모 측면에서 20배 이상 소요되었다. 따라서 우선적으로 OpenCL 엔진 기반 하드웨어 가속 기술 최적화 연구를 통해 스마트 시설환경 실시간 시뮬레이션 기술 도입을 위한 기술적 과제를 풀어갈 것이다.

• Journal of Internet of Things and Convergence
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• v.3 no.1
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• pp.13-19
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• 2017

In this paper, we designed and implemented Thing Connected-Green, a self-installing agricultural automation system capable of remote monitoring and control based on Low Power Wide Area communication technology (LPWA). Farming requires water, sunlight, soil, fertilizer, temperature control, etc., and these elements can be remotely monitored and controlled using an automated system. Using this system, it is possible to construct an agricultural automation system which can be optimized according to the kind of plant and cultivation environment from vinyl house to flower garden. The information gathered from the sensor is stored in the server through the gateway, and the optimal cultivation environment can be set and operated using the smart phone based on the big data.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.45-52
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• 2020

In the paper, we propose a system that measures various agricultural parameters that affect crop yield and monitors location information. According to an analysis by international organizations, 60% of the world's population lives on agriculture. In addition, 11% of the world's soil is used for growing crops. For this reason, agriculture plays an important role in national development. If a problem occurs in agriculture due to weather or environmental problems, it can be a problem for national development. In order to solve these problems, it is important to modernize agriculture using modern IoT technology. It is possible to improve the agricultural environment by applying IoT technology in agriculture to build a smart environment. Through such a smart environment, it is possible to increase the yield of agricultural products, reduce water waste, and prevent overuse of fertilizers. In order to verify the proposed system, an experiment was performed in a soybean cultivation farm. Experimental results showed that using the proposed system, the moisture in the cultivated soil can be automatically maintained at 40%.

• Journal of the Korea Society of Computer and Information
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• v.26 no.10
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• pp.139-145
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• 2021

In this paper, by using the cattle feed intake, rumination, and in heat monitoring technology, RFI (Residual Feed Intake) monitoring and wearable devices and PCs for predicting abnormalities in budding target web and smart A monitoring system using a phone application was designed and implemented. With the development of this system, the farmer is expected to increase economic efficiency. By analyzing the feed intake, it is possible to identify the difference between the recommended feed amount based on the cow's weight and the feed amount consumed by the cow, and it is expected that early detection of metabolic disorders (abnormality of metabolism) is possible. Farmers using the results of this thesis can distinguish the cows with the most efficient performance, and the 6-axis motion sensor signals input from the wearable device attached to the cow's skin (neck) and the microphone attached to the wearable device. It is possible to measure the cow's rumination and feed intake through the sound of the cow's throat. In the future, improvements will be made to measure additional vital signs such as heart rate and respiration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.485-488
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• 2016

The level of domestic greenhouse complex environmental control technology is a hardware-oriented automation steps that mechanically control the environments of greenhouse, such as temperature, humidity and $CO_2$ through the technology of cultivation and consulting experts. This automation brings simple effects such as labor saving. However, in order to substantially improve the output and quality of agricultural products, it is essential to track the growth and physiological condition of the plant and accordingly control the environments of greenhouse through a software-based complex environmental control technology for controlling the optimum environment in real time. Therefore, this paper is a part of general methods on the greenhouse complex environmental control technology. and presents a horticulture production forecasting methods using artificial neural networks through the analysis of big data systems of smart farm performed in our country and artificial neural network technology trends.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.125-130
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• 2021

Implementation of livestock smart-farm can be done more effectively with IoT technology developing. An build of useful stock management system can be possibile if push messages of these judgement are notified on smart-phone after cattle's illness and estrus are judged using IoT technology. These judgement method of cattle's illness and estrus can be done with gathering living stock data using temperature sensor and 3 axis acceleration sensor and sending these data using IoT and internet network into server, and studying AI machine learning using these data. In this paper, to build this cattle management system based on IoT, effective system of the whole architecture is showed. Also an effective analysis and design method to develop this system software will be presented by showing user requirement analysis using object-oriented method, flowchart and screen design.