• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1105-1112
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• 2022

Field data monitoring systems such as renewable energy generation and smart farm integrated control are developing from PC and server to mobile first, and various wireless communication and application services have emerged with the development of IoT technology. Low-power wide-area networks are services optimized for low-power, low-capacity, and low-speed data transmission, and data collected in the field is transmitted to designated storage servers or cloud-based data platforms, enabling data monitoring. In this paper, we implement an IoT repeater that collects field data with a single device and transmits it to a wireless carrier cloud data flat using a low-power wide-area network, and a monitoring app using it. Using this, the system configuration is simpler, the cost of deployment and operation is lower, and effective data accumulation is possible.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1611-1617
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• 2018

The body temperature of livestock is directly related to the health of livestock such that it changes immediately when there exists health problem. Accordingly, the monitoring of livestock's temperature is one of most important tasks in farm management. However, the temperature of livestock is usually measured using skin-attached sensor which is significantly affected by the outside temperature and the condition of attachment which results in the inaccurate measurement of temperature. Herein we have proposed new scheme which estimates the body core temperature of cow based on measured data from neck-attached smart sensor. Especially, we have considered both schemes which estimate the exact temperature and which detect the unusually high temperature based on machine learning. We have found that the occurrence of high temperature can be detected accurately. The proposed scheme can be used in monitoring of health condition of cow and improving the efficiency of farm management.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.65-71
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• 2019

Conventional methods of measuring ground resistance and ground resistance field measurement are used to measure voltage drop according to the resistance value of the site by applying current by installing a constant interval of measurement electrode. If the stratified structure of the site site is unique, errors in boundary conditions will occur in the event of back acid and the analysis of the critical ground resistance in the ground design will show much difference from simulation. This study utilizes the Arduino module and smart ground measurement technology in the convergent information and communication environment to develop a reliable smart land resistance measuring device even if the top layer of land is unique, to analyze the land resistance and accumulate data to predict the change in the age of the land. Considering the topographical characteristics of the site, we propose a ground resistance measuring device and its method of measuring ground resistance so that the auxiliary electrode can be installed by correctly positioning the angle and distance in measuring ground resistance. Not only is ground resistance value obtained through electrodes installed to allow accurate ground resistance values to be selected, but it can also be used as a useful material for installing electrical facilities in similar areas. Moreover, by utilizing reliable data and analyzing the large sections of the site, a precise analysis of the site, which is important in ground design as well as construction cost, is expected to be used much in ground facility design such as potential rise.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.75-82
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• 2021

Mastitis in cows is a major factor that hinders dairy productivity of farms, and many attempts have been made to solve it. However, research on mastitis has been limited to diagnosis rather than prediction, and even this is mostly using a single sensor. In this study, a predictive model was developed using multivariate data including biometric data and environmental data. The data used for the analysis were collected from robot milking machines and sensors installed in farmhouses in Chungcheongnam-do, South Korea. The recurrent neural network model using three weeks of data predicts whether or not mastitis is diagnosed the next day. As a result, mastitis was predicted with an accuracy of 82.9%. The superiority of the model was confirmed by comparing the performance of various data collection periods and various models.

• The Journal of Bigdata
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• v.8 no.1
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• pp.1-10
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• 2023

This paper presents the design process of an agricultural data sharing platform intended to address major challenges faced by the domestic agricultural industry. The platform was designed with a user interface that prioritizes user requirements for ease of use and offers various analysis techniques to provide growth prediction for field environment, growth, management, and control data. Additionally, the platform supports File to DB and DB to DB linkage methods to ensure seamless linkage between the platform and farmhouses. The UI design process utilized HTML/CSS-based languages, JavaScript, and React to provide a comprehensive user experience from platform login to data upload, analysis, and detailed inquiry visualization. The study is expected to contribute to the development of Korean smart farm models and provide reliable data sets to agricultural industry sites and researchers.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.27-40
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• 2022

Building self-sustainable rural infrastructure and environment through smart digital agriculture technology innovation is one of the major goals of the Korean agricultural administration as a part of the nation's 4^th industry revolution. To identify areas for improving and effectively investing in the acceleration of rural development, 207 experts in the areas of crop science and smart digital agriculture technology were interviewed for their opinions and suggestions on 22 questions designed to recognize fundamental agricultural issues to be addressed and solutions to advance technology innovation and rural development. Majority of the participants expected smart digital agriculture technologies to resolve major agricultural issues and help build a better rural environment. To overcome technology gaps and resolve issues more effectively, further investment in training new technology experts and building stronger agricultural technology infrastructure is urgent, and persistent and systematic support from agricultural administration appears to be the key for accelerating the process. While the leading global groups of both public and private sectors have advanced their technologies beyond the field application stage, most of the Korean technologies remain at the early pilot stage. Aging population and lack of labor in rural areas, unknown future climate change, and challenges in sustainable rural development are expected to be resolved by smart digital agriculture technologies. Technological innovations by research institutes should be promptly deployed in the crop production field, and farm training systemically organized by local technology centers can accelerate farming revolution. Standardization of equipment and data systems is another key to the success of digitalization of food crop production and food supply chains nationwide.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.159-159
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• 2017

스마트 시설환경은 대표적으로 원예, 축산 분야 등 여러 형태의 농업현장에 정보 통신 및 데이터 분석 기술을 도입하고 있는 시설화된 생산 환경이라 할 수 있다. 근래에 하드웨어적으로 급증한 스마트 시설환경에서 생산되는 방대한 생육/환경 데이터를 올바르고 적합하게 사용하기 위해서는 일반 산업 현장과는 차별화 된 분석기법이 요구된다고 할 수 있다. 소프트웨어 공학 분야에서 연구된 빅데이터 처리 기술을 기계적으로 농업 분야의 빅데이터에 적용하기에는 한계가 있을 수 있다. 시설환경 내/외부의 다양한 환경 변수는 시계열 데이터의 난해성, 비가역성, 불특정성, 비정형 패턴 등에 기인하여 예측 모델 연구가 매우 난해한 대상이기 때문이라 할 수 있다. 본 연구에서는 근래에 관심이 급증하고 있는 인공신경망 연구 소프트웨어인 Tensorflow (www.tensorflow.org)와 대표적인 Open source인 OpenNN (www.openn.net)을 스마트 시설환경 환경변수 상호간 상관성 분석에 응용하였다. 해당 소프트웨어 라이브러리의 운영환경을 살펴보면 Tensorflow 는 Linux(Ubuntu 16.04.4), Max OS X(EL capitan 10.11), Windows (x86 compatible)에서 활용가능하고, OpenNN은 별도의 운영환경에 대한 바이너리를 제공하지 않고 소스코드 전체를 제공하므로, 해당 운영환경에서 바이너리 컴파일 후 활용이 가능하다. 소프트웨어 개발 언어의 경우 Tensorflow는 python이 기본 언어이며 python(v2.7 or v3.N) 가상 환경 내에서 개발이 수행이 된다. 주의 깊게 살펴볼 부분은 이러한 개발 환경의 제약으로 인하여 Tensorflow의 주요한 장점 중에 하나인 고속 연산 기능 수행이 일부 운영 환경에 국한이 되어 제공이 된다는 점이다. GPU(Graphics Processing Unit)의 제공하는 하드웨어 가속기능은 Linux 운영체제에서 활용이 가능하다. 가상 개발 환경에 운영되는 한계로 인하여 실시간 정보 처리에는 한계가 따르므로 이에 대한 고려가 필요하다. 한편 근래(2017.03)에 공개된 Tensorflow API r1.0의 경우 python, C++, Java언어와 함께 Go라는 언어를 새로 지원하여 개발자의 활용 범위를 매우 높였다. OpenNN의 경우 C++ 언어를 기본으로 제공하며 C++ 컴파일러를 지원하는 임의의 개발 환경에서 모두 활용이 가능하다. 특징은 클러스터링 플랫폼과 연동을 통해 하드웨어 가속 기능의 부재를 일부 극복했다는 점이다. 상기 두 가지 패키지를 이용하여 2016년 2월부터 5월 까지 충북 음성군 소재 딸기 온실 내부에서 취득한 온도, 습도, 조도, CO2에 대하여 Large-scale linear model을 실험적(시간단위, 일단위, 주단위 분할)으로 적용하고, 인접한 세그먼트의 환경변수 예측 모델링을 수행하였다. 동일한 조건의 학습을 수행함에 있어, Tensorflow가 개발 소요 시간과 학습 실행 속도 측면에서 매우 우세하였다. OpenNN을 이용하여 대등한 성능을 보이기 위해선 병렬 클러스터링 기술을 활용해야 할 것이다. 오프라인 일괄(Offline batch)처리 방식의 한계가 있는 인공신경망 모델링 기법과 현장 보급이 불가능한 고성능 하드웨어 연산 장치에 대한 대안 마련을 위한 연구가 필요하다.

• 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