• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.184-192
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• 2021

Domestic agriculture is facing real problems, such as a decrease in the population in rural areas, a shortage of labor due to an aging population, and increased risks due to the deepening of climate change. Smart farming technology is being developed to solve these problems. In the development of smart agricultural technology, irrigation control plays an important role in creating an optimal growth environment and is an important issue in terms of environmental protection. This paper is about the study of collecting and analyzing the rhizosphere environmental data of domestic paprika farms for the purpose of improving the quality of crops, reducing production costs, and increasing production. Irrigation control modeling presented in this paper Control modeling is to graphically present changes in a medium weight, feed, and drainage due to regional climatic features. To derive the graph, the parameters were determined through data collection and analysis, and the suggested irrigation control modeling method was applied to the collected rhizosphere environmental data to control irrigation in 6 regions (Gangwon-do, Chungnam, Jeonbuk, Jeonnam, Gyeongbuk, and Gyeongnam). The parameters were obtained and graphs were derived from them. After that, a study was conducted to analyze the derived parameters to verify the validity of the irrigation control modeling method and to correlate them with climatic features (average temperature and precipitation).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.336-336
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• 2022

빗물자원의 대부분은 바다로 유입되어 소모되거나, 하수로 유입되어 불필요한 고도처리 공정이 진행되고, 하수처리장 용량에 과부하를 발생시키는 등 막대한 예산이 투입되고 있는 실정이다. 갈수기시 농가에서 용수를 확보하기 위한 용수 운반장치 등 기반구축이 쉽지 않으며, 인력 부족으로 정상적인 용수공급에 어려움을 겪고 있다. 이에 빗물자원을 용수로서 효율적으로 활용하기 위한 시스템의 구축이 필요하다. 본 연구는 빗물저류조에 스마트 관수제어 시스템을 적용한 것으로 지중에 설치된 토양수분 센서와 저류조 내부의 수위 센서에서 관측된 데이터를 토대로 자동으로 지중에 수분을 공급하는 시설이다. 지중에 수분이 부족할 경우 밸브를 열어 자동으로 펌프를 가동시켜 저류조 내부의 물을 지중으로 공급시키며 지중의 수분이 충분하거나 저류조 내부의 물이 부족해질 경우 밸브를 닫아 공급을 중단하도록 한다. 또한 저류조의 수위와 토양의 수분량, 펌프의 작동여부 등은 앱을 이용하여 실시간으로 확인이 가능하며, 스마트폰 앱을 이용한 수동조작 또한 가능하다. 본 기술은 집수, 저류, 공급, 통신, 제어, 센서 총 6종의 모듈로 구성되어 있으며, 사용자의 환경 및 예산의 따라 집수, 저류, 공급 모듈 등 맞춤형 제품 구성이 가능하도록 개발하였다. 저류조의 물공급을 관리, 제어하는 시스템으로 센서로부터 전송받은 데이터를 기준으로 펌프를 작동시켜 수분공급을 제어할 수 있으며 해당 기록을 서버에 저장하여 데이터의 통계를 구할 수 있도록 하였다. 사용자가 앱을 통하여 직접적인 제어 및 가동환경에 대한 설정을 할 수 있어 사용자가 직접 현장에 오지 않아도 토지의 현황과 저류조의 수위, 수분 공급상황 등을 직접 제어할 수 있도록 개발하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1373-1378
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• 2018

In order to solve the problems such as reduction of agriculture population, aging and declining of grain self sufficiency rate, agriculture ICT convergence technology utilizing IoT technology is actively being developed. Agricultural ICT technology only concentrates on facility houses, and there is no automated control system in the field of cultivation. In this paper, we propose an irrigation control system that automatically controls the solenoid valves and water pumps in a large area with Lora wireless communication. The proposed system does not require a separate power source by using a small solar panel, and it is very convenient to install and operate supporting wireless auto setup by plug-and-play method. Therefore, it is expected that it will contribute to the reduction of labor force, quality of agricultural products, and productivity improvement.

• 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.