• 한국농공학회논문집
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• 제66권4호
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• pp.17-26
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• 2024

This study aimed to design and build a web-based decision support system for wheat cultivation management. The system is designed to collect and measure the weather environment at the growth stage on a daily basis and predict the soil moisture content. Based on this, APSIM, one of the process-based crop models, was used to predict the potential yield of wheat cultivation in real time by making decisions at each stage. The decision-making system for wheat crop management was designed to provide information through a web-based dashboard in consideration of user convenience and to comprehensively evaluate wheat yield potential according to past, present, and future weather conditions. Based on the APSIM model, the system estimates the current yield using past and present weather data and predicts future weather using the past 40 years of weather data to estimate the potential yield at harvest. This system is expected to be developed into a decision support system for farmers to prescribe irrigation and fertilizer in order to increase domestic wheat production and quality by enhancing the yield estimation model by adding influence factors that can contribute to improving wheat yield.

• 한국농공학회논문집
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• 제64권6호
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• pp.35-41
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• 2022

Heavy snow damage can be prevented in advance with an appropriate security system. To develop the security system, we developed a model that predicts snow depth after a few hours when the snow depth is observed, and utilized it to calculate a failure probability with various types of greenhouses and observed snow depth data. We compared the Markov chain model and Bayesian long short-term memory models with varying input data. Markov chain model showed the worst performance, and the models that used only past snow depth data outperformed the models that used other weather data with snow depth (temperature, humidity, wind speed). Also, the models that utilized 1-hour past data outperformed the models that utilized 3-hour data and 6-hour data. Finally, the Bayesian LSTM model that uses 1-hour snow depth data was selected to predict snow depth. We compared the selected model and the shifting method, which uses present data as future data without prediction, and the model outperformed the shifting method when predicting data after 11-24 hours.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권11호
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• pp.3083-3098
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• 2023

As demands for efficient and echo-friendly production of marine products increase, smart aquaculture based on information and communication technology (ICT) has become a promising trend. The smart aquaculture is expected to control fundamental farm environment variables including water temperature and dissolved oxygen (DO) levels with less human intervention. A recirculating aquaculture system (RAS) is required for the smart aquaculture which utilizes a purification tank to reuse water drained from the water tank while blocking the external environment. Elaborate water treatment should be considered to properly operate RAS. However, analyzing the water treatment performance is a challenging issue because fish farm circumstance continuously changes and recursively affects water fluidity. To handle this issue, we introduce computational fluid dynamics (CFD) aided water treatment analysis including water fluidity and the solid particles removal efficiency. We adopt RAS parameters widely used in the real aquaculture field to better reflect the real situation. The simulation results provide several indicators for users to check performance metrics when planning to select appropriate RAS without actually using it which costs a lot to operate.

• 소성∙가공
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• 제32권3호
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• pp.153-159
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• 2023

This paper presents the optimal design of a heating system using radiant heating elements for application in smart farms. Smart farming, an advanced agricultural technology, is based on artificial intelligence and the internet of things and promotes crop production. Temperature and humidity regulation is critical in smart farms, and thus, a heating system is essential. Radiant heating elements are devices that generate heat using electrical energy. Among other applications, radiant heating elements are used for environmental control and heating in smart farm greenhouses. The performance of these elements is directly related to their electrical energy consumption. Therefore, achieving a balance between efficient electrical energy consumption and maximum heating performance in smart farms is crucial for the optimal design of radiant heating elements. In this study, the size, electrical energy supply, heat generation efficiency, and heating performance of radiant heating elements used in these heating systems were investigated. The effects of the size and electrical energy supply of radiant heating elements on the heating performance were experimentally analyzed. As the radiant heating element size increased, the heat generation efficiency improved, but the electrical energy consumption also increased. In addition, increasing the electrical energy supply improved both the heat generation efficiency and heating performance of the radiant heating elements. Based on these results, a method for determining the optimal size and electrical energy supply of radiant heating elements was proposed, and it reduced the electrical energy consumption while maintaining an appropriate heating performance in smart farms. These research findings are expected to contribute to energy conservation and performance improvement in smart farming.

• 한국전자통신학회논문지
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• 제13권4호
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• pp.851-858
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• 2018

본 논문에서는 스마트 농장의 경제성과 생산성을 제고하기 위한 스마트 농장을 위한 온습도 제어 알고리즘을 제안하였다. 스마트 농장의 기본 조건을 분석하고, 이를 기반으로 무선통신을 기반으로 하는 스마트 농장내의 센서 및 제어대상간 정보교환 시스템을 설계하였으며, 스마트 농장내의 온도, 습도 그리고 토양습도가 식물 성장에 적합하게 설정된 기준 값을 추종하도록 온습도 제어 알고리즘을 개발하였다. 제안된 설계 방법론 및 제어 알고리즘의 유효성을 검증하기 위해서 2.4GHz 무선통신 기반 소규모 스마트 농장의 프로토타입을 제작하였으며, 온습도 실험을 통해서 그들의 유효성을 확인하였다.

• 한국정보통신학회논문지
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• 제23권6호
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• pp.733-739
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• 2019

수위측정은 스마트 공장(Smart Factory), 스마트 농장(Smart Farm), 스마트 양식장(Smart Fish Farm) 등 IoT 모니터링 분야에서 활용도가 높다. 그러나 기존의 수위측정 방식은 복잡한 알고리즘과 고가의 센서 위주로 상품성과 산업현장 적용성이 떨어진다. 본 논문에서는 이러한 문제를 해결하고자 비접촉식 스마트센서인 적외선 거리 센서와 홀센서 기반 수위측정 방법을 각각 제안하였다. 센서의 고감도로 인해 발생하는 데이터 오류는 간단한 구조도입을 통해 해결함으로써 범용성을 높였다. 구현된 수위측정 방법은 성능평가 실험을 통해 그 유효성이 검증되었다. 본 연구를 통해 개발된 센서는 IoT 기술 기반 범용성 수위측정 모니터링 시스템으로 확장될 수 있을 것으로 기대한다.

• 한국정보전자통신기술학회논문지
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• 제12권3호
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• pp.290-298
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• 2019

농업 인구의 비중이 매년 점점 감소하고 있어 농촌의 고령화 현상은 점점 심각해질 것으로 보인다. 농업 인구의 노령층 증가와 청년층 축소가 진행되고 있고 농업 인구의 고령화가 점점 심각해지고 있어서 농업인력이 부족하다. 농업 인력의 부족은 농업 농촌의 문제가 될 것이고 대책방안의 마련이 필요하다. 그리고 기존의 시설재배는 단위면적당 생산/수확량에 집중되어 있었다. 하지만, 지금은 생산량뿐만 아니라 작물의 품질도 좋아야 가격경쟁력을 확보할 수 있다. 그래서 작물의 생산성을 높이면서 에너지는 절감 시킬 수 있는 ICT기반 시설원예의 첨단화 스마트 온실 보급을 정책적으로 도입해야 한다. 이에 스마트 온실을 통하여 농작물 및 온실 환경에 대한 모니터링을 하고 농작물의 생장 환경을 제어할 수 있는 복합형 알고리즘을 이용한 사물인터넷 기반의 데이터를 확보하면서 스마트팜 서비스를 위한 시스템의 개발이 필요한 것으로 보인다.따라서 본 연구에서는 작물의 근권환경부의 생장환경 데이터를 무선으로 수집하고 수집한 데이터를 스마트폰으로 모니터링 할 수 있는 스마트팜 융합서비스 시스템의 개발을 목적으로 한다.

• 품질경영학회지
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• 제52권2호
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• pp.201-220
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• 2024

Purpose: Smart farming is related to the low carbon certification system as it provides many opportunities to cultivate and manage crops in an eco-friendly, thereby reducing carbon footprint. However, there is a significant lack of consumer perception research on low carbon labels for smart farms vegetables. Therefore, this study aims to investigate consumer perceptions of smart farm vegetable and low carbon labels. Methods: This study manipulated cultivation type(general vs. smart farm) and low carbon labels (yes vs. no) as experimental stimuli. Measurement questions and the research model were validated through confirmatory factor analysis and reliability analysis. Hypotheses testing were conducted using SPSS 29.0, AMOS 28.0. Results: The results of the study showed no significant difference in consumers perceived naturalness based on cultivation types, and there was also no moderating effect of the low carbon label. There was no difference between environmental benefits and health benefits according to the cultivation type. Perceived naturalness had a significant effect on both environmental and health benefits, and environmental benefits showed a higher impact relationship. These benefits positively affected attitudes and willingness to pay a premium, Environmental benefits had a higher impact on attitudes, while health benefits had a higher impact on willingness to pay a premium. Lastly, attitudes were found to have a significant impact on the willingness to pay a premium. Conclusion: This study is valuable in that it investigated consumer perceptions of smart farms and low carbon labels that have not been previously studied. It compares the environmental and health benefits, confirming their influence on attitudes and willingness to pay a premium. The results suggest a potential expansion in academic research on smart farming and low carbon labels, offering practical insights for marketing strategies and policies for relevant companies.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2019년도 전력전자학술대회
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• pp.543-545
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• 2019

Recently, the demand for smart farms is increasing due to the increase in the cultivation area such as horticulture, fruit trees and special crops. However, due to the irregular weather changes and the cultivation method of the crops due to the different cultivation environment, there are frequent occurrence of diseases and insect pests and infectious diseases due to system error or carelessness, and the cycle is also very short. In addition, the Smart Farm business has been built by combining various sensors (temperature, humidity, CO2, illumination) and LED lighting, but it is costly in terms of frequent errors, lack of power supply, And thus the management can not be efficiently managed. Therefore, this paper combines real time sensing technology based on IoT Platform and high performance control technology to control pests and equipment errors and monitor the growth status of crops in real time based on big data analysis and Artificial Intelligence System.

• 전기전자학회논문지
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• 제23권1호
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• pp.322-325
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• 2019

비간섭 전력부하 감시 알고리즘은 메인선의 총 전력 사용량만으로 개별 전기 기기들의 전력 사용량을 추론할 수 있다. 이런 알고리즘을 개발하기 위해서는 개별 기기의 전력사용 패턴 데이터와 이들 기기의 다양한 조합 전력사용 패턴 데이터가 필요하다. 본 논문에서는 스마트팜용 비간섭 전력부하 감시 알고리즘개발에 필요한 전력사용 패턴 데이터 취득시스템 개발방법을 제시하였다. 데이터 취득시스템은 매초마다 개별 전기 기기들의 전력 사용량과 다양한 조합의 시나리오에 따른 전력 사용량을 동시에 측정할 수 있도록 되어 있다. 측정된 데이터는 LTE 망을 통하여 스마트팜 외부에서 원격으로 모니터링이 가능하며, 측정된 데이터는 외부 서버에 저장된다.