• Title/Summary/Keyword: Digital agriculture

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Research on Construction Strategy of Agricultural Digital Twins (농업 디지털 트윈 구축 전략에 대한 연구)

  • Han jae Keem;Jun young Do;Yong-Hwan Lee
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.1
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    • pp.79-83
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    • 2024
  • Digital Twin technology is rapidly transforming various industries by providing comprehensive virtual models that replicate physical objects or processes. In the context of agriculture, digital twin can be a game-changer. This technology can help in creating precise simulations of farming scenarios, thereby enabling farmers to make data-driven decisions and optimize farm operations. The potential benefits include improved crop yields, resource efficiency, and environmental sustainability. However, the implementation of digital twin technology in agriculture poses challenges, such as data management issues and the need for robust IoT infrastructure. Despite these hurdles, the future of digital twin in agriculture looks promising, with ongoing research and developments aimed at overcoming these obstacles.

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Study on Establishment of the Greenhouse Environment Monitoring System for Crop Growth Monitoring (작물 생식 모니터링을 위한 온실환경 모니터링 시스템 구축연구)

  • Kim, Won-Kyung;Cho, Byeong-Hyo;Hong, Youngki;Choi, Won-Sik;Kim, Kyoung-Chul
    • Journal of the Korean Society of Industry Convergence
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    • v.25 no.3
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    • pp.349-356
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    • 2022
  • Currently, the agricultural population in Korea indicates a decreasing and aging orientation. As the population of farm labor continues to decline, so farmers are feeling the pressure to be stable crop production. To solve the problem caused by the decreasing of farm labor, it is necessary to change over to "Digital agriculture". Digital agriculture is tools that digitally collect, store, analyze, and share electronic data and/or information in agriculture, and aims to integrate the several digital technologies into crop and livestock management and other processes in agriculture fields. In addition, digital agriculture can offer the opportunity to increase crop production, save costs for farmer. Therefore, in this study, for data-based Digital Agriculture, a greenhouse environment monitoring system for crop growth monitoring based on Node-RED, which even beginners can use easily, was developed, and the implemented system was verified in a hydroponic greenhouse. Several sensors, such as temperature, humidity, atmospheric pressure, CO2, solar radiation, were used to obtain the environmental data of the greenhouse. And the environmental data were processed and visualized using Node-RED and MariaDB installed in rule.box digital. The environment monitoring system proposed in this study was installed in a hydroponic greenhouse and obtained the environmental data for almost two weeks. As a result, it was confirmed that all environmental data were obtained without data loss from sensors. In addition, the dashboard provides the names of installed sensors, real time environmental data, and changes in the last three days for each environmental data. Therefore, it is considered that farmers will be able to easily monitor the greenhouse environment using the developed system in this study.

A Study on Environmental Factor Recommendation Technology based on Deep Learning for Digital Agriculture (디지털 농업을 위한 딥러닝 기반의 환경 인자 추천 기술 연구)

  • Han-Jin Cho
    • Smart Media Journal
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    • v.12 no.5
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    • pp.65-72
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    • 2023
  • Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

Digital Transformation of Agriculture Supply Chain in Vietnam: Current Status and Proposal of Roadmap

  • Quoc Cuong Nguyen;Hoang Tuan Nguyen
    • International journal of advanced smart convergence
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    • v.13 no.2
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    • pp.249-257
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    • 2024
  • As the main driver of economic growth and employment, the agricultural sector plays an important role in Vietnam's economy. However, in recent years, the sector has faced new challenges and also presented new investment opportunities to stimulate agricultural growth. Many Vietnamese agricultural producers currently lack the modern technology and decision support tools needed to maintain and improve productivity in a rapidly changing environment. Other stakeholders in the agricultural value chain, such as input suppliers, distributors, and consumers, also face significant challenges, including disrupted value chains, transportation costs. The cost of transporting goods across the supply chain continues to increase and information exchange remains fragmented. A potential solution to address these challenges is the application of digital transformation in agricultural supply chains. Farmers and other value chain participants can improve the production of their goods and procedures by utilizing new and cutting-edge technologies that are integrated into a unified system as part of the digital transformation of agricultural supply chains. In this study, we evaluate the current status of digital transformation in the supply chain of the agriculture industry by finding and examining pertinent publications from key agencies as well as prior research. From there, in the framework of the digital economy, this study suggests a digital transformation roadmap for the agricultural supply chain.

Economic impact of digitalization on agriculture: a Korean perspective

  • Jung-Won Youm;Su-Hwan Myeong;Jeong-Ho Yoo
    • Korean Journal of Agricultural Science
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    • v.49 no.1
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    • pp.31-43
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    • 2022
  • The global trade environment is rapidly changing. The spread of COVID-19 promotes digitalization, and online transactions are becoming the new normal. Currently, Korea is actively introducing information and communication technology (ICT) that uses the internet of things (IoT) in relation to agriculture. However, few studies have analyzed the impact of digitalization on trade in the agricultural sector. Thus, the purpose of this study is to examine how the introduction of digital technology can affect the economy and trade of Korea. In this study, we estimate the impact of introducing digital technologies using the computable general equilibrium (CGE) model. The results of this analysis indicate that the GDP could increase by 3.82% to 10.53%. Also, agricultural production and trade according to the model will significantly increase to 8.67% and 5.72%, respectively, through a productivity increase from Blockchain, IoT, and artificial intelligence (AI) technologies, despite logistics inefficiencies. Although the effects of digitalization could be significant, farmers are still struggling to introduce digital technologies, stemming from the fact that government support systems are concentrated in only a few sub-sectors. In this regard, support in this area must be expanded and diversified according to the current environment of agriculture in Korea.