• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.344-344
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• 2020

기후변화 가속화와 국민의 높아진 서비스 요구 수준에 따라 농업용수의 관리방식을 현장인력의 경험적 물관리 방식에서 계측정보 기반의 과락적 물관리 방식으로 전환의 필요성이 대두되어, 2001년부터 농업기반시설 내 무인계측, 원격제어 기능이 탑재된 물관리자동화 시스템을 보급하는 농업용수관리자동화사업을 시행하였다. 농업용수관리자동화사업은 사업시행 초기 연구 결과, 농업기반시설 무인계측 및 원격제어 시스템 보급으로 인력에 의한 관행적 물관리 대비 수리시설의 관리 효율성이 크게 향상되어 유지관리 인력의 절감 및 용수수급의 적정성이 개선될 것으로 분석되었다. 하지만 영농환경의 변화에 따라, 당초 분석결과와 달리 자동화사업 추진과 한국농어촌공사의 유지관리 인력 규모 간 뚜렷한 상관성이 보이지 않는다는 정책기관의 지적이 발생하고 있다. 현재 4차산업기술이 산업 전 분야에 걸쳐 일어나고 있으며 농업분야에도 ICT, LOT, 빅데이터 기술이 도입되어 새로운 가치를 창출하고 있다. 농업용수관리 분야에 있어서는 데이터를 활요한 수요자 중심의 지능형 물관리 사업이 추진되고 있으며, 일정규모 이상 저수지 및 양수장 농업용수 공급량 측정 계측기의 설치가 추진중에 있다. 그러나 현재까지 이러한 설치된 계측장치들의 활용방안에 대해서는 뚜렷한 결과가 도축된 바 없으며, 현재 많은 예산과 인력이 투입되어 설치·운영되고 있는 계측장치들의 활용 방안에 대해서 연구가 필요한 실정이다. 2018년 2,228개 농업기반시설물에 자동화시스템을 설치 완료 하였으나, 각종 장비의 비표준화, 효과대비 고비용, 잦은 통신두절 등의 기술적 문제로 인해 현업부서의 수자원관리 업무에서 자동화시스템의 활용성이 저조한 것으로 관측됐다. 본연구에서는 국내 수자원 계측제어 기술 동향 및 운영환경 조사 결과를 기초로, 기술적 측면의 농업용수관리자동화사업의 개선사항과 4차 산업기술의 농업용수관리자동화사업의 적용방안을 제시 하여 농업용수관리자동화사업 중장기 계획 개정등 향후 정챡수립시 참고 자료로의 활용과 농업용수 효율적 활용과 관리를 위한 TM/TC 미래추진 방안을 제시로 정확하고 신뢰도 높은 농업용수 관리 체계를 구축 하고자 한다.

• Research in Plant Disease
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• v.26 no.3
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• pp.121-133
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• 2020

Climate change, increased extreme weather and climate events, and rapidly changing socio-economic environment threaten agriculture and thus food security of our society. Therefore, it is urgent to shift from conventional farming to smart agriculture using big data and artificial intelligence to secure sustainable growth. In order to efficiently manage plant diseases through smart agriculture, agricultural big data that can be utilized with various advanced technologies must be secured first. In this review, we will first learn about agrometeorological big data consisted of meteorological, environmental, and agricultural data that the plant pathology communities can contribute for smart plant disease management. We will then present each sequential components of the smart plant disease management, which are prediction, monitoring and diagnosis, control, prevention and risk management of plant diseases. This review will give us an appraisal of where we are at the moment, what has been prepared so far, what is lacking, and how to move forward for the preparation of smart plant disease management.

• Proceedings of the Korean Society of Crop Science Conference
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• 2020.11a
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• pp.132-132
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• 2020

아직까지 국내에서 GM작물이 상업화를 위해 승인된 예는 없지만 생명공학기술의 발전으로 GM작물의 개발은 급속한 증가 추세에 있다. 비의도적인 방출로 인해 미승인 LMO 목화가 전국적으로 재배되어 국립종자원 주관으로 양성 판정된 재배지의 목화를 폐기 처분하였으나(2017), GM작물이 유해하다는 인식과 환경에 방출되어 생태계를 교란시킨다는 인식이 팽배해 있는 현실에서 과학적으로 유전자의 이동성을 검증하는 노력이 중요하다. 자식성 작물의 화분의 이동성 조사를 위해 중앙의 코어 위치에 LM작물을 식재한 후 LM작물 주변에 재배품종을 심어 유전자이동 가능성을 조사하고 재배 환경에 의한 영향을 평가하기 위해 포장 주변 기상상황 데이터-온도, 습도, 풍속, 풍향, 기압, 강수량 등을 분석하고 기상상황이 화분의 전이에 미치는 영향 조사하였다.

• 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 Bio-Environment Control
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• v.4 no.2
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• pp.246-252
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• 1995

작물, 가축, 농산물을 학문의 대상으로 하는 농학은 기상, 토양 등과 같은 자연 현상으로부터 필요한 데이터를 획득하여 이용한다. 그러나, 이들 데이터는 많은 환경 요인의 영향을 받아 그 거동이 매우 복잡한 비선형적 현상을 나타내는 것이 대부분이다. 따라서, 실험을 통해 획득된 데이터의 처리 및 모형화 등을 위해 기존의 수학적, 통계적 방법을 이용하는 경우에 많은 어려움을 겪게 된다. 이에 최근에는 신경회로망 및 퍼지 이론 등과 같은 인공 지능 기법을 이용하여 이러한 문제점을 해결하기 위한 연구가 활발히 진행되고 있다. 본 강좌에서는 복잡한 비선형 특성 특히 임의적 거동을 보이는 자연 현상을 기술하기 위해 최근에 대두되고 있는 혼돈 이론에 대한 소개를 하고자 한다.(중략)

• Smart Media Journal
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• v.12 no.10
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• pp.47-54
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• 2023

As understanding sudden climate change and agricultural productivity becomes increasingly important due to global warming, soil moisture prediction is emerging as a key topic in agriculture. Soil moisture has a significant impact on crop growth and health, and proper management and accurate prediction are key factors in improving agricultural productivity and resource management. For this reason, soil moisture prediction is receiving great attention in agricultural and environmental fields. In this paper, we collected and analyzed open field environmental data using a pilot field through random forest, a machine learning algorithm, obtained the correlation between data characteristics and soil moisture, and compared the actual and predicted values of soil moisture. As a result of the comparison, the prediction rate was about 92%. It was confirmed that the accuracy was . If soil moisture prediction is carried out by adding crop growth data variables through future research, key information such as crop growth speed and appropriate irrigation timing according to soil moisture can be accurately controlled to increase crop quality and improve productivity and water management efficiency. It is expected that this will have a positive impact on resource efficiency.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.251-259
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• 2018

BACKGROUND: Various culture media have been used for hydroponic cultures of horticultural plants under the smart greenhouses with natural and artificial light types. Management of the culture medium for the control of medium amounts and/or necessary components absorbed by plants during the cultivation period is performed with ICT (Information and Communication Technology) and/or IoT (Internet of Things) in a smart farm system. This study was conducted to develop the cloud-based data analysis system for effective management of culture medium applying to hydroponic culture and plant growth in smart greenhouses. METHODS AND RESULTS: Conventional inorganic Yamazaki and organic media derived from agricultural byproducts such as a immature fruit, leaf, or stem were used for hydroponic culture media. Component changes of the solutions according to the growth stage were monitored and plant growth was observed. Red and green lettuce seedlings (Lactuca sativa L.) which developed 2~3 true leaves were considered as plant materials. The seedlings were hydroponically grown in the smart greenhouse with fluorescent and light-emitting diodes (LEDs) lights of $150{\mu}mol/m^2/s$ light intensity for 35 days. Growth data of the seedlings were classified and stored to develop the relational database in the virtual machine which was generated from an open stack cloud system on the base of growth parameter. Relation of the plant growth and nutrient absorption pattern of 9 inorganic components inside the media during the cultivation period was investigated. The stored data associated with component changes and growth parameters were visualized on the web through the web framework and Node JS. CONCLUSION: Time-series changes of inorganic components in the culture media were observed. The increases of the unfolded leaves or fresh weight of the seedlings were mainly dependent on the macroelements such as a $NO_3-N$, and affected by the different inorganic and organic media. Though the data analysis system was developed, actual measurement data were offered by using the user smart device, and analysis and comparison of the data were visualized graphically in time series based on the cloud database. Agricultural management in data visualization and/or plant growth can be implemented by the data analysis system under whole agricultural sites regardless of various culture environmental changes.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.303-304
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• 2022

현재까지 IoT 관련 기술들은 수많이 발전해왔다. 하지만 IoT 관련 기술들이 농업에 적용된 사례는 많지 않다. 이로 인해 농업에서는 자동화로 대체가 가능한 노동들이 여전히 사람들이 직접 하고 있다. 본 논문은 농업에 종사하시는 분들의 편의성 증대와 함께 농촌의 부족한 노동력을 충족시키기 위해 센서들을 이용하여 자동화된 농업 시스템인 '온실 환경 자동 케어 시스템'을 제안한다. 기존의 사람의 노동력을 이용한 방식이 아닌 컴퓨터가 센서와 상호작용을 하여 데이터를 처리하고 온실을 제어하여 농업 종사자들의 편의성을 증대시켜 나아가 농업의 부족한 노동력을 충족 시킬 수 있다.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.249-267
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• 2021

The use of big data accumulated along with the progress of digitalization is bringing disruptive innovation to the global agricultural industry. Recently, the government is establishing an agricultural big data platform and a support organization. However, in the domestic agricultural industry, the use of big data is insufficient except for some companies in the field of cultivation and growth. In this context, this study identifies factors affecting the intention to use big data in terms of technology, organization and environment, and also confirm the moderating effect of technical field, focusing on agricultural ventures which should be the main entities in creating innovation by using big data. Research data was obtained from 309 agricultural ventures supported by the A+ Center of FACT(Foundation of AgTech Commercialization and Transfer), and was analyzed using IBM SPSS 22.0. As a result, Among technical factors, relative advantage and compatibility were found to have a significant positive (+) effect. Among organizational factors, it was found that management support had a positive (+) effect and cost had a negative (-) effect. Among environmental factors, policy support were found to have a positive (+) effect. As a result of the verification of the moderating effect of technology field, it was found that firms other than cultivation had a moderating effect that alleviated the relationship between all variables other than relative advantage, compatibility, and competitor pressure and the intention to use big data. These results suggest the following implications. First, it is necessary to select a core business that will provide opportunities to generate new profits and improve operational efficiency to agricultural ventures through the use of big data, and to increase collaboration opportunities through policy. Second, it is necessary to provide a big data analysis solution that can overcome the difficulties of analysis due to the characteristics of the agricultural industry. Third, in small organizations such as agricultural ventures, the will of the top management to reorganize the organizational culture should be preceded by a high level of understanding on the use of big data. Fourth, it is important to discover and promote successful cases that can be benchmarked at the level of SMEs and venture companies. Fifth, it will be more effective to divide the priorities of core business and support business by agricultural venture technology sector. Finally, the limitations of this study and follow-up research tasks are presented.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.