National Investment Framework for Revitalizing the R&D Collaborative Ecosystem of Sustainable Smart Agriculture
Abstract
:1. Introduction
1.1. Background and Literature Review
1.1.1. Background of Smart-Farming Policies in Korea
- First stage (2015–2018): Convenience improvement (more convenient and remote control)
- Second stage (2019–2020): Productivity improvement (less input and more automatic control of water supply and temperature according to the set environment)
- Third stage (2021–): Sustainability improvement (anyone can operate a farm with AI-based on high production and high-quality accumulated data)
- Smart agriculture aims to prepare a sustainability strategy for agriculture in response to factors such as climate change crises, food crises caused by population growth, limited resource utilization, and carbon emission. It employs advanced ICT (AI and big data) to improve agricultural productivity and quality, remotely or automatically manage the cultivation environment of crops and livestock and reduce the labor force via a national innovative growth strategy for sustainable future agriculture.
- Precision agriculture is the oldest agricultural concept and includes technology for detailed monitoring of farmland and water supply and nutrients in the right place. The core technology of precision agriculture is open-field farming, which involves the cultivation of food crops, vegetables, and fruit trees.
- Smart farming is a core technology of facility farming, including plant gardening facilities, such as greenhouses and plastic houses, livestock facilities for mass breeding of livestock, and plant factories that are closed plant cultivation facilities using artificial light. Smart-farm technology includes technologies to monitor the growth and breeding environment of crops and livestock in facility farms using the Internet of things (IoT), big data, and AI and make optimal farming decisions.
- Digital agriculture includes technology that collects, analyzes, and shares data on the agriculture and livestock industry and traces the entire process of production, processing, logistics, distribution, and consumption. Digital agriculture can be largely divided into fields such as digital agriculture data platform; digital agriculture distribution, logistics, and consumption; and data solutions and service technologies. For distribution and logistics in the agricultural and livestock industry, various ICTs such as big data, IoT, AI, and cloud computing are combined to implement a smart production and logistics system and smart shops. Figure 2 depicts these concepts.
1.1.2. Theoretical and Empirical Literature Review
1.1.3. Research Purpose and Questions
2. Materials and Methods
2.1. Data Collection and Preprocessing
2.2. Clustering Process
2.3. Definition of Research Areas Related to Smart Agriculture
2.4. Targeted Collaborative Research Area: Strawberries
3. Results
3.1. Nationally-Funded Projects Regarding Smart Agriculture
- Goals of Cluster 1 (Crops and Livestock): Crop Production, Growth, Livestock Growth, and Health Management Technology for Smart Agriculture. It included technologies for measuring crop growth and physiology and detecting the presence of pathogens, identifying pests and diseases.
- Goals of Cluster 2 (Smart Energy): Renewable Energy Utilization Technology for Agricultural Power Generation for Smart Agriculture. It covered technologies to maintain and manage homeostasis in optimal conditions using minimal (renewable) energy.
- Goals of Cluster 3 (Agri-Food and Supply Platform): Integrated Management Platform (Distribution, Logistics, and Consumption) for Digital Agriculture. It implied a platform that optimizes efficient management and marketing by sharing information about producers, consumers, and logistics companies.
- Goals of Cluster 4 (Data·Network·AI): AI for Digital Agriculture. It contained technologies that collect real-time big data in facility horticulture or livestock and optimize environmental conditions in the AI algorithms.
- Goals of Cluster 5 (Agricultural Machinery): Smart Agricultural Machinery and Agricultural Drone for Precision Agriculture. It included technologies that utilize agricultural machinery and robots and collect data from agricultural sites with imaging equipment and sensors mounted on unmanned aerial vehicles.
- Goals of Cluster 6 (Farm Robots): AI Farmbots for Smart Farms. It covered technologies that can autonomously perform optimal agricultural work, as per the situation, by analyzing the status of crops and livestock.
- Goals of Cluster 7 (Environmental Information): Complex Environmental Information Measurement and Control Technology for Smart Agriculture. It included technologies to measure external factors such as temperature, humidity, and air quality.
- Goals of Cluster 8 (Plant Factory): Urban Agriculture Technology, including Indoor Vertical Farming System or Plant Factory for Smart Farms. It included technology to design, control, and utilize complex facilities and equipment to realize the prelude for crop and livestock production activities in a completely closed space.
3.2. Status of Government Investment in Smart Agriculture
3.2.1. Investment Status of Korean Government-Funded Projects in Smart Agriculture
3.2.2. Status and Trend of Public R&D Projects by Technology Cluster of Smart Agriculture
3.2.3. Status and Trend of Government R&D Investment in Smart Agriculture from the Perspective of the Time Phase
3.2.4. Status and Trend of Government R&D Investment in Smart Agriculture from the Perspectives of the Region and Stakeholders
3.3. Strategic Directions of R&D Investment for Smart Agriculture from a Regional Perspective: Strawberry
3.3.1. Status of Government-Funded Project Investment by Region Regarding Strawberries
3.3.2. Status of Government-Funded Projects for Strawberry from the Perspectives of Technology Clusters, Stakeholders, and Regions
4. Discussion
4.1. R&D Investment Strategy and Collaborative Ecosystem Framework for Sustainable Smart Agriculture in Korea
4.2. Conclusions
4.3. Limitations and Further Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- O’Shaughnessy, S.A.; Kim, M.; Lee, S.; Kim, Y.; Kim, H.; Shekailo, J. Towards smart farming solutions in the U.S. and South Korea: A comparison of the current status. Geogr. Sustain. 2021, 2, 312–327. [Google Scholar] [CrossRef]
- Rasul, G. A framework for addressing the twin challenges of COVID-19 and climate change for sustainable agriculture and food security in South Asia. Front. Sustain. Food Syst. 2021, 5, 1–16. [Google Scholar] [CrossRef]
- Fu, X.; Zhou, Y.; Yang, F.; Ma, L.; Long, H.; Zhong, Y.; Ni, P. A review of key technologies and trends in the development of integrated heating and power systems in agriculture. Entropy 2021, 23, 260. [Google Scholar] [CrossRef] [PubMed]
- Klerkx, L.; Jakku, E.; Labarthe, P. A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS Wageningen J. Life Sci. 2019, 90, 100315:1–100315:16. [Google Scholar] [CrossRef]
- Kritikos, M. Precision Agriculture in Europe: Legal, Social and Ethical Considerations; European Parliamentary Research Service: Brussels, Belgium, 2017. [Google Scholar]
- Farm Europe. Global Food Forum: A New Ambition for EU Agri-Food Systems; Farm Europe: Brussels, Belgium, 2017. [Google Scholar]
- Bleich, S.N.; Moran, A.J.; Vercammen, K.A.; Frelier, J.M.; Dunn, C.G.; Zhong, A.; Fleischhacker, S.E. Strengthening the public health impacts of the supplemental nutrition assistance program through policy. Annu. Rev. Public Health 2019, 41, 453–480. [Google Scholar] [CrossRef] [Green Version]
- Sanders, C.E.; Gibson, K.E.; Lamm, A.J. Rural broadband and precision agriculture: A frame analysis of united states federal policy outreach under the Biden administration. Sustainability 2022, 14, 460. [Google Scholar] [CrossRef]
- Yoon, D. The Abe administration’s growth strategy: Policy idea, institutional change, and state-driven policy governance. Seoul J. Jpn. Stud. 2018, 4, 65–101. [Google Scholar]
- ECOS GmbH; Temmen, N. Smart Farming Technology in Japan and Opportunities for EU Companies; EU-Japan Centre for Industrial Cooperation: Tokyo, Japan, 2021. [Google Scholar]
- Nagasaki, Y. Realization of Society 5.0 by Utilizing Precision Agriculture into Smart Agriculture in NARO, Japan. Available online: https://ap.fftc.org.tw/article/1414 (accessed on 1 March 2022).
- Ministry of Agriculture, Food and Rural Affairs (MAFRA). The 2nd Comprehensive Plan; MAFRA: Sejong, Korea, 2017.
- MAFRA. The 3rd Comprehensive Plan; MAFRA: Sejong, Korea, 2020.
- Kim, S.; Choi, S. Innovative Platform Program: Current Status and Economic Effect; National Assembly Budget Office: Seoul, Korea, 2020. [Google Scholar]
- Ministry Concerned. Smart Farm Expansion Plan; MAFRA: Sejong, Korea, 2018.
- Ministry Concerned. The Comprehensive Measures for the Spread of Smart Agriculture Based on Big Data and Artificial Intelligence; MAFRA: Sejong, Korea, 2021.
- Ministry Concerned. Agri-Food Carbon Neutral Promotion Strategy; MAFRA: Sejong, Korea, 2021.
- Christiaensen, L.; Rutledge, Z.; Taylor, J.E. Viewpoint: The future of work in agri-food. Food Policy 2021, 99, 101963. [Google Scholar] [CrossRef]
- Lufumpa, C.L.; Shimeless, A.; Kamgnia, B.; Salami, A. Korean experiences in agricultural development and policy proposals for structural transformation of African agriculture and rural space (STAARS). Afr. Econ. Brief 2016, 7, 1–11. [Google Scholar]
- Nam, G.-P.; Jeon, S. A study on the promotion direction of smart arable farming of Korea agricultural co-operatives. Coop. Econ. Manag. Rev. 2021, 55, 143–165. [Google Scholar] [CrossRef]
- Lim, Y.; Lee, J.; Park, D.; Kim, S.; Sim, S.; Choo, S.; Kim, Y. Identification of Domestic and Overseas Smart Rural-Related Policy Trends and Key Policy Agenda: Focusing on Smart Infrastructure Integration Policy; Korea Rural Economic Institute (KREI): Naju, Korea, 2019. [Google Scholar]
- Jooryang, L.; Soo-jin, C.; Young-hoon, I.; Dong-bae, P.; Seong-cheol, S.; Gaeun, K. The S&T Policy Study on Extension of Smart-Farming in Korea; Science & Technology Policy Institute(STEPI): Sejong, Korea, 2018. [Google Scholar]
- Lee, D.; Kim, K. Information analysis framework for supporting evidence-based research and development policy: Practical considerations for rationality in the policy process. Informatiz. Policy 2021, 28, 77–93. [Google Scholar] [CrossRef]
- OECD. Perspectives on Decentralisation and Rural-Urban Linkages in Korea; OECD: Paris, France, 2021. [Google Scholar] [CrossRef]
- Yoo, G.; Yeo, C. Smart Agriculture; Korea Institute of Science & Technology Evaluation and Planning (KISTEP): Eumseong, Korea, 2021. [Google Scholar]
- Lee, S.H. The Importance of ICT Technology Development and Smart Agriculture in the Participation of Korean Small-Scale Farmers in the Value Chain. Available online: https://ap.fftc.org.tw/article/2744 (accessed on 14 April 2022).
- MAFRA. Smart Farm Innovation Valley in Sangju Started Operation as an Innovation Hub for Talents and Technologies; MAFRA: Sejong, Korea, 2021; pp. 32–33.
- Jang, Y.; Kim, T. Status and Tasks of Smart Farm Expansion and Dissemination Project: Focusing on ICT Convergence Project in Agricultural Field; National Assembly Research Service (NARS): Seoul, Korea, 2019. [Google Scholar]
- Sniazhko, S. Uncertainty in decision-making: A review of the international business literature. Cogent Bus. Manag. 2019, 6, 1–32. [Google Scholar] [CrossRef]
- Dewulf, A.; Biesbroek, R. Nine lives of uncertainty in decision-making: Strategies for dealing with uncertainty in environmental governance. Policy Soc. 2018, 37, 441–458. [Google Scholar] [CrossRef] [Green Version]
- Bradshaw, A.G.A.; Borchers, J.G.; Ecology, S.C.; Jul, N. Uncertainty as information narrowing the science-policy gap. Conserv. Ecol. 2020, 4, 7. [Google Scholar] [CrossRef] [Green Version]
- EVIPNet Europe. Situation Analysis on Evidence-Informed Evidence Brief Situation for Policy Evidence-Informed Policy-Making; EVIPNet Europe: Copenhagen, Denmark, 2017. [Google Scholar]
- Cash, D.; Clark, W.C.; Alcock, F.; Dickson, N.; Eckley, N.; Jäger, J. Salience, Credibility, Legitimacy and Boundaries: LINKING Research, Assessment and Decision Making; KSG Working Papers Series; John, F., Ed.; Kennedy School of Government, Harvard University: Cambridge, MA, USA, 2003; Available online: http://nrs.harvard.edu/urn-3:HUL.InstRepos:32067415 (accessed on 14 April 2022).
- Pichancourt, J.-B.; Bauer, R.; Billard, A.; Brennan, M.; Caurla, S.; Colin, A.; Contini, A.; Cosgun, S.; Cuny, H.; Dumarçay, S.; et al. A Generic information framework for decision-making in a forest-based bio-economy. Ann. For. Sci. 2021, 78, 97. [Google Scholar] [CrossRef]
- Guo, H.; Nativi, S.; Liang, D.; Craglia, M.; Wang, L.; Schade, S.; Corban, C.; He, G.; Pesaresi, M.; Li, J.; et al. Big Earth data science: An information framework for a sustainable planet. Int. J. Digit. Earth 2020, 13, 743–767. [Google Scholar] [CrossRef] [Green Version]
- Abdel-Basset, M.; Mohamed, R.; Sallam, K.; Elhoseny, M. A novel decision-making model for sustainable supply chain finance under uncertainty environment. J. Clean. Prod. 2020, 269, 122324. [Google Scholar] [CrossRef]
- Maes, J.; Egoh, B.; Willemen, L.; Liquete, C.; Vihervaara, P.; Schägner, J.P.; Grizzetti, B.; Drakou, E.G.; Notte, A.L.; Zulian, G.; et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 2012, 1, 31–39. [Google Scholar] [CrossRef]
- van den Honert, R. Improving decision making about natural disaster mitigation funding in Australia—A framework. Resources 2016, 5, 28. [Google Scholar] [CrossRef] [Green Version]
- Stacey, D.; Murray, M.A.; Légaré, F.; Sandy, D.; Menard, P.; O’Connor, A. Decision coaching to support shared decision making: A framework, evidence, and implications for nursing practice, education, and policy. Worldviews Evid.-Based Nurs. 2008, 5, 25–35. [Google Scholar] [CrossRef]
- Claxton, K.; Sculpher, M.; Drummond, M. A rational framework for decision making by the National Institute for Clinical Excellence (NICE). Lancet 2002, 360, 711–715. [Google Scholar] [CrossRef]
- Sculpher, M.; Gafni, A.; Watt, I. Shared treatment decision making in a collectively funded health care system: Possible conflicts and some potential solutions. Soc. Sci. Med. 2002, 54, 1369–1377. [Google Scholar] [CrossRef]
- Stafinski, T.; Menon, D.; McCabe, C.; Philippon, D.J. To fund or not to fund: Development of a decision-making framework for the coverage of new health technologies. Pharmacoeconomics 2011, 29, 771–780. [Google Scholar] [CrossRef] [PubMed]
- Cerri, K.H.; Knapp, M.; Fernandez, J.L. Public funding of pharmaceuticals in the Netherlands: Investigating the effect of evidence, process and context on CVZ decision-making. Eur. J. Health Econ. 2014, 15, 681–695. [Google Scholar] [CrossRef] [PubMed]
- Chan, K.; Nam, S.; Evans, B.; Deoliveira, C.; Chambers, A.; Gavura, S.; Hoch, J.; Mercer, R.E.; Dai, W.F.; Beca, J.; et al. Developing a framework to incorporate real-world evidence in cancer drug funding decisions: The Canadian real-world evidence for value of cancer drugs (CanREValue) collaboration. BMJ Open 2020, 10, e032884. [Google Scholar] [CrossRef] [Green Version]
- Meadmore, K.; Fackrell, K.; Recio-Saucedo, A.; Bull, A.; Fraser, S.D.S.; Blatch-Jones, A. Decision-making approaches used by UK and international health funding organisations for allocating research funds: A survey of current practice. PLoS ONE 2020, 15, e0239757. [Google Scholar] [CrossRef]
- Streed, A.; Kantar, M.; Tomlinson, B.; Raghavan, B. How sustainable is the smart farm? In Proceedings of the Workshop on Computing within Limits, Online, 14–15 June 2021. [Google Scholar] [CrossRef]
- Wolfert, S.; Ge, L.; Verdouw, C.; Bogaardt, M.-J. Big data in smart farming—A review. Agric. Syst. 2017, 153, 69–80. [Google Scholar] [CrossRef]
- Ayre, M.; McCollum, V.; Waters, W.; Samson, P.; Curro, A.; Nettle, R.; Paschen, J.-A.; King, B.; Reichelt, N. Supporting and practising digital innovation with advisers in smart farming. NJAS Wageningen J. Life Sci. 2019, 90, 1–12. [Google Scholar] [CrossRef]
- Macrae, R.J.; Hill, S.B.; Henning, J.; Bentley, A.J. Policies, programs, and regulations to support the transition to sustainable agriculture in Canada. Am. J. Altern. Agric. 1990, 5, 76–92. [Google Scholar] [CrossRef]
- Berthet, E.T.; Hickey, G.M.; Klerkx, L. Opening design and innovation processes in agriculture: Insights from design and management sciences and future directions. Agric. Syst. 2018, 165, 111–115. [Google Scholar] [CrossRef]
- Noor, N.H.M.; Ng, B.K.; Hamid, M.J.A. Forging researchers-farmers partnership in public social innovation: A case study of Malaysia’s agro-based public research institution. Int. Food Agribus. Manag. Rev. 2020, 23, 579–597. [Google Scholar] [CrossRef]
- Dale, A.; Marshall, A. New directions for facilitating quality agricultural development in Northern Queensland. Australas. J. Reg. Stud. 2020, 26, 269–292. [Google Scholar]
- Bachev, H. State and evolution of public and private research and development in Bulgarian agriculture. Int. J. Sustain. Dev. World Policy 2020, 9, 10–25. [Google Scholar] [CrossRef] [Green Version]
- Adamashvili, N.; Fiore, M.; Contò, F.; La Sala, P. Ecosystem for Successful Agriculture. Collaborative Approach as a Driver for Agricultural Development. Eur. Countrys. 2020, 12, 242–256. [Google Scholar] [CrossRef]
- Adamashvili, N.; State, R.; Tricase, C.; Fiore, M. Blockchain-based wine supply chain for the industry advancement. Sustainability 2021, 13, 13070. [Google Scholar] [CrossRef]
- Mogues, T.; Yu, B.; Fan, S.; Mcbride, L. The Impacts of Public Investment in and for Agriculture Synthesis of the Existing Evidence; ESA Working Paper; International Food Policy Research Institute (IFPRI): Washington, DC, USA, 2012. [Google Scholar] [CrossRef]
- Dwyer, J. Transformation for sustainable agriculture: What role for the second pillar of CAP? Bio-Based Appl. Econ. 2013, 2, 29–47. [Google Scholar]
- Barnes, A.P. Towards a framework for justifying public agricultural R&D: The example of UK agricultural research policy. Res. Policy 2001, 30, 663–672. [Google Scholar] [CrossRef]
- Stojanova, S.; Lentini, G.; Niederer, P.; Egger, T.; Cvar, N.; Kos, A.; Duh, E.S. Smart villages policies: Past, present and future. Sustainability 2021, 13, 1663. [Google Scholar] [CrossRef]
- Lee, D.; Kim, K. Public R&D projects-based investment and collaboration framework for an overarching South Korean national strategy of personalized medicine. Int. J. Environ. Res. Public Health 2022, 19, 1291. [Google Scholar] [CrossRef]
- Lee, D.; Kim, K. Research and development investment and collaboration framework for the hydrogen economy in South Korea. Sustainability 2021, 13, 10686. [Google Scholar] [CrossRef]
- Lee, D.; Kim, K. A collaborative trans-regional R&D strategy for the South Korea Green New Deal to achieve future mobility. Sustainability 2021, 13, 8637. [Google Scholar] [CrossRef]
- Lee, D.; Heo, Y.; Kim, K. A strategy for international cooperation in the COVID-19 pandemic era: Focusing on national scientific funding data. Healthcare 2020, 8, 204. [Google Scholar] [CrossRef] [PubMed]
- Lee, D.; Kim, S.; Kim, K. International R&D collaboration for a global aging society: Focusing on aging-related national-funded projects. Int. J. Environ. Res. Public Health 2020, 17, 8545. [Google Scholar] [CrossRef]
- Lee, D.; Kang, J.; Kim, K. Global collaboration research strategies for sustainability in the post COVID-19 era: Analyzing virology-related national-funded projects. Sustainability 2020, 12, 6561. [Google Scholar] [CrossRef]
- Rural Development Administration. The Penetration Rate of Domestic Varieties of Korean Strawberries; RDA: Jeonju, Korea, 2022. [Google Scholar]
- Mikkola, J.H. Portfolio management of R&D projects: Implications for innovation management. Technovation 2001, 21, 423–435. [Google Scholar] [CrossRef]
Region | Unique Identification Number (ID) | Organization | Type of Organization | Funding (Thousand USD) | Project Period | Project Content | ||
---|---|---|---|---|---|---|---|---|
Start Date | End Date | Title | Abstract | |||||
Jeollanam-do | 1415176355 | ELSYS Co., Ltd. Naju, South Korea | Industry | 2300,000,000 | 1 May 2019 | 31 December 2022 | Development and demonstration of renewable energy convergence system for crops | LoRaWAN multi-channel gateway hardware design and production, LoRaWAN multi-channel gateway software development or implementation, low-power Internet of Things hardware and software requirements analysis, energy convergence brokerage service design and development, analysis and design of energy, convergence brokerage platform requirements, energy convergence brokerage trading platform mobile application development |
Jeollabuk-do | 1395069779 | National Academy of Agricultural Sciences | Research institute | 130,000,000 | 1 January 2021 | 31 December 2023 | Field application and advancement of smart insect pollination on a strawberry and tomato smart farm | Existing (prototype) customized smart beehive sensing system design, smart beehive entry-level and high-end smart system design, improvement and advancement of image processing for bee activity measurement (maintaining algorithm, improving platform, and camera), development of modularization technology for both low-level (simple) and advanced types of beehive internal environment sensing technology, simple modularization (beehive internal temperature, humidity, hive weight, and activity recorder), advanced modularization (e.g., beehive internal temperature, humidity, carbon dioxide, food quantity, weight, activity recorder, and fan system for ventilation), and development of low-power sensing technology for field application of fruit trees (e.g., kiwis) for digital agriculture |
Search Terms | Time Period | Number of Raw Data Items | Number of Data Items Utilized |
---|---|---|---|
(“smart farm *” OR “smart agriculture *” OR “precision farm *” OR “precision agriculture *” OR “precision livestock *” OR “livestock farm *” OR ”digital farm *” OR “digital agriculture *” OR “smart management information system” OR “plant factory” OR “vertical farm *” OR ((“big data” OR digital OR “internet of thing *” OR “IoT” OR “artificial intelligence” OR precision OR vertical OR urban) AND (agriculture * OR crop * OR farm * OR greenhouse * OR fruit * OR vegetable * OR plant * OR livestock * OR husbandry OR animal OR cultiva * OR culture * OR harvest * OR breed *))) | 2015–2021 | 6961 | 5646 (strawberry: 157) |
Region | Funding (Thousand USD) | No. of Projects | Funding Per Project | Funding (%) |
---|---|---|---|---|
Gangwon-do | 20,125 | 217 | 93 | 3.0% |
Gyeonggi-do | 85,700 | 666 | 129 | 12.7% |
Gyeongsangnam-do | 39,826 | 437 | 91 | 5.9% |
Gyeongsangbuk-do | 33,652 | 371 | 91 | 5.0% |
Gwangju | 32,061 | 239 | 134 | 4.8% |
Daegu | 32,497 | 234 | 139 | 4.8% |
Daejeon | 57,554 | 338 | 170 | 8.5% |
Busan | 17,319 | 130 | 133 | 2.6% |
Seoul | 115,042 | 768 | 150 | 17.1% |
Sejong | 1794 | 26 | 69 | 0.3% |
Ulsan | 2275 | 12 | 190 | 0.3% |
Incheon | 10,757 | 78 | 138 | 1.6% |
Jeollanam-do | 44,363 | 332 | 134 | 6.6% |
Jeollabuk-do | 100,289 | 1125 | 89 | 14.9% |
Jeju | 19,341 | 136 | 142 | 2.9% |
Chungcheongnam-do | 35,661 | 260 | 137 | 5.3% |
Chungcheongbuk-do | 26,365 | 277 | 95 | 3.9% |
Total | 674,622 | 5646 | 119 | 100.0% |
Smart Agriculture | Technology Cluster | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Total | % |
---|---|---|---|---|---|---|---|---|---|---|
Protected Agriculture | Crops and livestock (CLS_1) | 20.4 | 26.0 | 27.9 | 25.4 | 27.8 | 26.7 | 37.3 | 191.4 | 28.4% |
Smart energy (CLS_2) | 7.8 | 10.1 | 9.6 | 9.2 | 10.4 | 23.7 | 17.3 | 88.1 | 13.1% | |
Farm robots (CLS_6) | 0.7 | 1.6 | 3.8 | 5.7 | 9.5 | 11.5 | 7.6 | 40.7 | 6.0% | |
Environmental information (CLS_7) | 5.4 | 4.4 | 6.0 | 10.8 | 11.6 | 11.1 | 12.7 | 62.0 | 9.2% | |
Plant factory (CLS_8) | 8.4 | 4.2 | 1.5 | 2.2 | 4.4 | 4.8 | 6.6 | 32.1 | 4.8% | |
Open-Field Agriculture | Agricultural machinery (CLS_5) | 6.9 | 10.3 | 8.8 | 14.4 | 16.1 | 21.9 | 38.9 | 117.2 | 17.4% |
Digital Agriculture | Data·network·artificial intelligence (CLS_4) | 9.2 | 14.3 | 14.0 | 11.9 | 18.9 | 18.5 | 14.5 | 101.5 | 15.0% |
Agri-food platform (CLS_3) | 6.4 | 7.7 | 7.3 | 6.8 | 5.0 | 4.4 | 4.2 | 41.8 | 6.2% | |
Total Sum (Unit: million USD) | 65.2 | 78.5 | 78.9 | 86.4 | 103.7 | 122.7 | 139.1 | 674.6 | 100.0% |
Smart Agriculture | Technology Cluster | Phase 1 Total (2015–2018) | Phase 2 Total (2019–2021) | Phase 1 CAGR (2015–2018) | Phase 2 CAGR (2019–2021) | Total CAGR (2015–2021) |
---|---|---|---|---|---|---|
Protected Agriculture (Smart Farm) | Crops and livestock (CLS_1) | 99.6 | 91.8 | 7.6% | 15.9% | 10.6% |
Smart energy (CLS_2) | 36.7 | 51.4 | 5.6% | 29.0% | 14.1% | |
Farm robots (CLS_6) | 11.9 | 28.7 | 101.3% | −10.5% | 48.8% | |
Environmental information (CLS_7) | 26.6 | 35.4 | 25.8% | 4.5% | 15.2% | |
Plant factory (CLS_8) | 16.3 | 15.8 | -36.2% | 22.6% | −3.9% | |
Open-Field Agriculture (Precision Agriculture) | Agricultural machinery (CLS_5) | 40.3 | 76.9 | 27.9% | 55.5% | 33.5% |
Digital Agriculture | Data·network·artificial intelligence (CLS_4) | 49.5 | 52.0 | 8.9% | −12.4% | 7.9% |
Agri-food platform (CLS_3) | 28.2 | 13.6 | 2.1% | −8.6% | −6.8% | |
Total Sum (Unit: million USD) | 280.8 | 352.0 | 9.8% | 15.8% | 13.5% |
Regions (Unit: Million USD) | Protected Agriculture | Open-field Agriculture | Digital Agriculture | Total | |||||
---|---|---|---|---|---|---|---|---|---|
Crops and Livestock (CLS_1) | Smart Energy (CLS_2) | Farm Robots (CLS_6) | Environmental Information (CLS_7) | Plant Factory (CLS_8) | Agricultural Machinery (CLS_5) | Data·Network·Artificial Intelligence (CLS_4) | Agri-Food Platform (CLS_3) | ||
Gangwon-do | 5.4 | 3.0 | - | 3.2 | 1.0 | 3.0 | 2.7 | 1.9 | 20.1 |
Gyeonggi-do | 28.6 | 11.5 | 3.0 | 7.1 | 3.0 | 10.9 | 16.2 | 5.3 | 85.7 |
Gyeongsangnam-do | 12.4 | 6.4 | 0.9 | 5.5 | 1.1 | 8.1 | 4.5 | 0.9 | 39.8 |
Gyeongsangbuk-do | 8.6 | 1.3 | 3.2 | 3.3 | 6.2 | 8.8 | 1.8 | 0.6 | 33.7 |
Gwangju | 4.6 | 8.1 | 4.9 | 2.8 | 0.5 | 5.2 | 4.4 | 1.7 | 32.1 |
Daegu | 5.4 | 1.2 | 4.5 | 1.3 | 0.2 | 14.3 | 4.7 | 0.7 | 32.5 |
Daejeon | 13.9 | 11.3 | 4.5 | 5.3 | 0.8 | 6.7 | 6.6 | 8.3 | 57.6 |
Busan | 6.6 | 1.6 | 3.2 | 1.4 | 0.2 | 1.3 | 0.4 | 2.5 | 17.3 |
Seoul | 37.6 | 6.3 | 5.7 | 8.2 | 10.2 | 11.5 | 29.4 | 6.1 | 115.0 |
Sejong | 0.7 | 0.6 | - | 0.1 | - | - | 0.2 | 0.1 | 1.8 |
Ulsan | 1.3 | - | - | 0.4 | - | 0.3 | 0.3 | - | 2.3 |
Incheon | 3.4 | 1.6 | 1.2 | 1.2 | - | 2.5 | 0.8 | - | 10.8 |
Jeollanam-do | 10.5 | 13.8 | 0.5 | 3.3 | 0.3 | 5.4 | 7.1 | 3.5 | 44.4 |
Jeollabuk-do | 29.6 | 5.4 | 4.5 | 8.6 | 7.1 | 20.6 | 16.1 | 8.3 | 100.3 |
Jeju | 3.4 | 7.0 | 0.3 | 6.3 | - | 0.6 | 1.1 | 0.7 | 19.3 |
Chungcheongnam-do | 11.6 | 6.5 | 2.7 | 2.3 | 0.2 | 8.7 | 3.2 | 0.4 | 35.7 |
Chungcheongbuk-do | 7.8 | 2.5 | 1.5 | 1.5 | 1.1 | 9.2 | 1.9 | 0.9 | 26.4 |
Total | 191.4 | 88.1 | 40.7 | 62.0 | 32.1 | 117.2 | 101.5 | 41.8 | 674.6 |
(Unit: Thousand USD) | Organization | Gangwon-do | Gyeonggi-do | Gyeongsangnam-do | Gyeongsangbuk-do | Gwangju | Daegu | Daejeon | Busan | Seoul | Sejong | Ulsan | Incheon | Jeollanam-do | Jeollabuk-do | Jeju | Chungcheongnam-do | Chungcheongbuk-do |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Crops and livestock (CLS_1) | Industry | 2555 | 13,369 | 696 | 2195 | 1675 | 2004 | 4545 | 529 | 12,476 | 492 | 1217 | 2905 | 2377 | 2956 | 984 | 6921 | 2174 |
University | 1659 | 2593 | 4783 | 1450 | 2540 | 3024 | 3307 | 5092 | 17,322 | 111 | 42 | 450 | 4136 | 6902 | 1554 | 572 | 3473 | |
Institute | 1061 | 10,844 | 6925 | 1438 | 383 | 385 | 6075 | 613 | 6730 | - | - | - | 2167 | 15,673 | 799 | 4088 | 2156 | |
Misc. | 127 | 1828 | - | 3541 | - | - | - | 408 | 1092 | 121 | - | - | 1789 | 4058 | 21 | - | - | |
Smart energy (CLS_2) | Industry | 1197 | 6367 | 4550 | 1241 | 7930 | 333 | 1639 | 1453 | 3060 | 621 | - | 1209 | 12,673 | 665 | 745 | 4567 | 2388 |
University | 1466 | 377 | 358 | 16 | 134 | 823 | 1128 | 192 | 2405 | - | - | 433 | 466 | 817 | 6217 | 350 | - | |
Institute | 292 | 3798 | 1488 | - | - | 83 | 8498 | - | 821 | - | - | - | 392 | 3911 | 50 | 1625 | 83 | |
Misc. | - | 983 | - | - | - | - | - | - | - | - | - | - | 225 | - | - | - | - | |
Farm robots (CLS_6) | Industry | - | 2336 | 541 | 468 | 1229 | 4528 | 554 | 3181 | 4658 | - | - | 1200 | 409 | 814 | 333 | 1433 | 1362 |
University | - | 629 | 83 | 302 | 3680 | - | 3422 | - | 1080 | - | - | - | - | 1411 | - | 83 | 117 | |
Institute | - | - | 263 | 2448 | - | - | 550 | - | - | - | - | - | 67 | 1987 | - | 1175 | - | |
Misc. | - | - | - | - | - | - | - | - | - | - | - | - | 42 | 267 | - | - | - | |
Environmental information (CLS_7) | Industry | 2033 | 5984 | 1553 | 1741 | 1727 | 528 | 2258 | 346 | 3735 | - | 447 | 989 | 2413 | 1208 | 1027 | 1024 | 424 |
University | 766 | 1001 | 443 | 1133 | 1058 | 815 | 1354 | 1067 | 3313 | - | - | 250 | 100 | 922 | 5208 | 878 | 558 | |
Institute | 413 | 158 | 3460 | 283 | - | - | 1731 | - | 650 | - | - | - | 628 | 6335 | 42 | 438 | 505 | |
Misc. | - | - | 3 | 98 | - | - | - | - | 500 | 100 | - | - | 168 | 158 | - | - | - | |
Plant factory (CLS_8) | Industry | 775 | 2833 | 465 | 2722 | 438 | 158 | 658 | 211 | 1696 | - | - | - | - | 1682 | - | 21 | 729 |
University | - | 40 | 556 | 579 | 17 | 83 | - | - | 2875 | - | - | - | 292 | 2951 | - | 197 | 358 | |
Institute | 250 | 142 | 100 | 2659 | - | - | 167 | - | 5641 | - | - | - | 25 | 2501 | - | - | 17 | |
Misc. | - | - | - | 217 | - | - | - | - | - | - | - | - | - | - | - | - | - | |
Agricultural machinery (CLS_5) | Industry | 1860 | 6392 | 4706 | 6182 | 1955 | 11,506 | 1250 | 728 | 3057 | - | 287 | 1709 | 3863 | 6531 | 49 | 7644 | 7269 |
University | 732 | 1223 | 1959 | 754 | 3247 | 2420 | 3282 | 592 | 5586 | - | - | 648 | 260 | 2816 | 278 | 151 | 1833 | |
Institute | 371 | 2341 | 1446 | 1820 | - | 257 | 2043 | - | 2465 | - | - | 117 | 283 | 10,268 | 247 | 946 | 119 | |
Misc. | - | 954 | - | 33 | - | 144 | 167 | - | 366 | - | - | - | 1042 | 1013 | - | - | - | |
Data·network·artificial intelligence (CLS_4) | Industry | 951 | 13,796 | 200 | 1381 | 409 | 3281 | 3293 | 368 | 6398 | 3 | 283 | 848 | 3547 | 4653 | 993 | 639 | 995 |
University | 538 | 529 | 2700 | 340 | 3951 | 339 | 717 | 3 | 4994 | - | - | - | 992 | 1210 | - | 262 | 492 | |
Institute | 1192 | 1559 | 1633 | 50 | - | 1078 | 2318 | 33 | 17,925 | - | - | - | 1930 | 7305 | 100 | 2277 | 425 | |
Misc. | - | 350 | - | - | - | - | 283 | - | 74 | 229 | - | - | 630 | 2959 | - | - | - | |
Agri-food platform (CLS_3) | Industry | 713 | 4525 | 410 | - | 675 | 458 | 688 | 263 | 2882 | - | - | - | 1625 | 1173 | 353 | 291 | 490 |
University | 468 | 106 | 200 | 200 | 478 | 250 | 6677 | 1403 | 3241 | 117 | - | - | 377 | 144 | 153 | - | 167 | |
Institute | 629 | 550 | 231 | 166 | 533 | - | 950 | 838 | - | - | - | - | 1305 | 6556 | 189 | 79 | 233 | |
Misc. | 76 | 92 | 75 | 192 | - | - | - | - | - | - | - | - | 144 | 446 | - | - | - | |
Total | Industry | 10,084 | 55,602 | 13,121 | 15,931 | 16,039 | 22,796 | 14,886 | 7078 | 37,962 | 1116 | 2234 | 8859 | 26,906 | 19,683 | 4484 | 22,540 | 15,831 |
University | 5630 | 6498 | 11,083 | 4774 | 15,105 | 7754 | 19,886 | 8349 | 40,817 | 228 | 42 | 1781 | 6622 | 17,172 | 13,409 | 2493 | 6996 | |
Institute | 4208 | 19,393 | 15,545 | 8865 | 917 | 1803 | 22,332 | 1483 | 34,232 | - | - | 117 | 6795 | 54,533 | 1427 | 10,628 | 3538 | |
Misc. | 203 | 4207 | 78 | 4082 | - | 144 | 450 | 408 | 2032 | 450 | - | - | 4040 | 8901 | 21 | - | - |
Regions | (Unit: Thousand USD) | Ratio |
---|---|---|
Gangwon-do | 350.8 | 3.1% |
Gyeonggi-do | 477.5 | 4.2% |
Gyeongsangnam-do | 2502.4 | 22.1% |
Gyeongsangbuk-do | 552.5 | 4.9% |
Daegu | 733.3 | 6.5% |
Daejeon | 150.0 | 1.3% |
Seoul | 750.0 | 6.6% |
Jeollanam-do | 3095.8 | 27.3% |
Jeollabuk-do | 2004.6 | 17.7% |
Chungcheongnam-do | 658.3 | 5.8% |
Chungcheongbuk-do | 58.3 | 0.5% |
Total | 11,333.6 | 100.0% |
(Unit: Thousand USD) | Types of Organizations | Protected Agriculture | Open-Field Agriculture | Digital Agriculture | Total | ||||
---|---|---|---|---|---|---|---|---|---|
Crops and Livestock (CLS_1) | Smart Energy (CLS_2) | Environmental Information (CLS_7) | Plant Factory (CLS_8) | Agricultural Machinery (CLS_5) | Data·Network·Artificial Intelligence (CLS_4) | Agri-Food Platform (CLS_3) | |||
Jeollanam-do | Industry | 665 | - | - | - | - | 167 | 793 | 1625 |
University | 165 | - | - | 25 | - | - | 127 | 317 | |
Institutes | 232 | - | 183 | 25 | - | 108 | 197 | 746 | |
Misc. | 189 | - | 75 | - | - | - | 144 | 408 | |
Sub-total | 1251 | - | 258 | 50 | - | 275 | 1261 | 3096 | |
Gyeongsangnam-do | Industry | - | - | - | - | 283 | - | - | 283 |
University | 225 | - | - | - | - | - | - | 225 | |
Institutes | 1740 | - | 254 | - | - | - | - | 1994 | |
Misc. | - | - | - | - | - | - | - | - | |
Sub-total | 1965 | - | 254 | - | 283 | - | - | 2502 | |
Jeollabuk-do | Industry | - | - | - | - | - | - | - | - |
University | 133 | - | - | - | - | - | - | 133 | |
Institutes | 892 | 167 | 146 | - | - | 375 | - | 1580 | |
Misc. | 167 | - | 125 | - | - | - | - | 292 | |
Sub-total | 1192 | 167 | 271 | - | - | 375 | - | 2005 | |
Sub-total of three regions | Industry | 665 | - | - | - | 283 | 167 | 793 | 1908 |
University | 524 | - | - | 25 | - | - | 127 | 676 | |
Institutes | 2864 | 167 | 583 | 25 | - | 483 | 197 | 4319 | |
Misc. | 356 | - | 200 | - | - | - | 144 | 700 | |
Sub-total | 4409 | 167 | 783 | 50 | 283 | 650 | 1261 | 7603 | |
Total strawberries by organization | Industry | 1473.50 | 170.83 | 350.83 | 708.33 | 283.33 | 166.67 | 792.83 | 3946 |
University | 523.75 | - | 545.83 | 254.17 | - | 116.67 | 126.83 | 1567 | |
Institutes | 3168.50 | 306.67 | 582.88 | 25.00 | - | 741.67 | 197.08 | 5022 | |
Misc. | 355.50 | - | 298.33 | - | - | - | 144.42 | 798 | |
Sub-total | 5521 | 478 | 1778 | 988 | 283 | 1025 | 1261 | 11,334 | |
Total strawberries by year | 2015 | 1246 | - | 83 | 25 | - | - | - | 3369 |
2016 | 1307 | 44 | 51 | 33 | - | 42 | 108 | 3601 | |
2017 | 912 | 217 | 375 | 33 | - | 42 | 160 | 3755 | |
2018 | 443 | 217 | 443 | 33 | - | 67 | 382 | 3603 | |
2019 | 338 | - | 298 | 158 | 117 | 267 | 382 | 3578 | |
2020 | 668 | - | 348 | 242 | 167 | 608 | 230 | 4282 | |
2021 | 608 | - | 179 | 463 | - | - | - | 3271 | |
Sub-total | 5521 | 478 | 1778 | 988 | 283 | 1025 | 1261 | 11,334 |
Region | Type of Organization | Organization | R&D Title | R&D Spectrum | Project Manager | Funding (Thousand USD) |
---|---|---|---|---|---|---|
Gyeongsangnam-do | University | Gyeongsang National University | Practical infrastructure development based on information on space movement and mutual exchange of strawberry flower-biome | Applied | Yeon-Sik Kwak | 225 |
Institutes | National Institute of Horticultural and Herbal Science | Study on the growth characteristics according to the temperature of the cooling, heating, and irrigation water during partial cooling and heating for high-bed strawberry | Experimental | Jong-Pil Moon | 150 | |
Institutes | National Institute of Horticultural and Herbal Science | The development of a hanging-bed culture system in greenhouse strawberry | Experimental | Myung-Hwan Cho | 185.83 | |
Institutes | National Institute of Horticultural and Herbal Science | The development of a hanging-bed culture system in greenhouse strawberry | Experimental | Lee Han-cheol | 170 | |
Industry | Daisys Co., Ltd. Daegu, South Korea | Smart-farm development and demonstration suitable for night and (melons and watermelons) and strawberry cultivation in Dandong greenhouses | Experimental | Kim Ki-hwan | 316.67 | |
Industry | Dongin Co., Ltd. Jinju, South Korea | Development of electric cultivator for strawberry high-rise reclamation | Experimental | Donghoon Kang | 283.33 | |
Jeollanam-do | University | Mokpo National University | Closed strawberry seedling demonstration advancement and economic analysis | Basic | Park Kyung-seop | 25 |
University | Sunchon National University | Development of an empirical model for the 6th industrialization of Jeonnam strawberry based on ICT convergence | Experimental | Chang-Sun Shin | 291.67 | |
Institutes | Gangjingun Agricultural Research & Extension Services | Development of vitality technology to produce excellent strawberry seedlings | Experimental | Young-Jun Choi | 183.33 | |
Institutes | Damyanggun Agricultural Research & Extension Services | Development of an empirical model for the 6th industrialization of Jeonnam strawberry based on ICT convergence | Experimental | Cheol-Gyu Lee | 166.67 | |
Institutes | Jeollabuk-do Agricultural Research & Extension Services | Development of an empirical model for the 6th industrialization of Jeonnam strawberry based on ICT convergence | Experimental | Gil-Ho Shin | 90 | |
Institutes | Jeollabuk-do Agricultural Research & Extension Services | The establishment of a supply system for rapid propagation and early dissemination of new strawberry cultivars | Experimental | Jong-Boon Seo | 25 | |
Institutes | Jeollabuk-do Agricultural Research & Extension Services | Field demonstration and enhancement of optimal growth control model for smart-farm strawberry and tomato in Jeonnam province | Applied | Kyung-Cheol Cho | 108.33 | |
Industry | ELSYS Co., Ltd. Naju, South Korea | Development of an empirical model for the 6th industrialization of Jeonnam strawberry based on ICT convergence | Experimental | Kyung-Woo Oh | 750 | |
Industry | ELSYS Co., Ltd. | Bear gray room building export energy savings for disease control in strawberry cultivation-type environmental management and disease forecasting/reporting system | Basic | Yo-Han Kim | 166.67 | |
Industry | Green Contro System Co., Ltd. Gwangju, South Korea | Development of fruit vegetable (tomato, paprika, and strawberry) growth management program using a growth model | Applied | Im-Sung Bae | 166.67 | |
Industry | One’s berry Co., Ltd. Damyang, South Korea | Development of an empirical model for the 6th industrialization of Jeonnam strawberry based on ICT convergence | Experimental | Doo-Hyun Yoon | 541.67 | |
Miscellaneous | Korea Greenhouse Crop Research Institute | Development of an empirical model for the 6th industrialization of Jeonnam strawberry based on ICT convergence | Experimental | Beom-Seok Seo | 333.33 | |
Miscellaneous | Korea Greenhouse Crop Research Institute | Development and demonstration of environmental control optimization technology for high-productivity strawberry greenhouse | Basic | Beom-Seok Seo | 75 | |
Jeollabuk-do | University | Jeonbuk National University | Strawberry disease diagnosis web UI advancement and expert utilization system establishment | Experimental | Jun-Hwan Lee | 133.33 |
Institutes | National Institute of Agricultural Sciences | Development of smart environment control system for growing strawberry greenhouse | Applied | Han Gil-soo | 145.83 | |
Institutes | National Institute of Agricultural Sciences | Development of an energy-saving system for growing strawberries | Applied | Jong-Pil Moon | 83.33 | |
Institutes | National Institute of Agricultural Sciences | Development of transplanting method and flowering promotion techniques for export strawberry | Applied | Jong-Pil Moon | 81.67 | |
Institutes | National Institute of Agricultural Sciences | Development of control method for a bacterial angular spot of strawberry | Basic | In-Sik Myung | 41.67 | |
Institutes | National Institute of Agricultural Sciences | Developed and demonstrate a responsive web UI for strawberry disease based on a cloud system | Experimental | Jeong-Hyun Baek | 41.67 | |
Institutes | National Institute of Horticultural and Herbal Science | Demonstration of strawberry cultivation using an innovative cooling house that overcomes high temperatures and research on optimal management technology | Applied | Dae-Young Kim | 291.67 | |
Institutes | National Institute of Horticultural and Herbal Science | The study of optimizing the cultivated environment of strawberries on a two-floor bed system | Basic | Seung-Yu Kim | 269.17 | |
Institutes | National Institute of Horticultural and Herbal Science | Image collection and DB upgrade for strawberry disease diagnosis AI training | Experimental | Jong-Han Park | 33.33 | |
Institutes | National Institute of Horticultural and Herbal Science | Development of an energy-saving system for growing strawberries | Applied | Jin-Kyung Kwon | 83.33 | |
Institutes | National Institute of Horticultural and Herbal Science | Development of transplanting method and flowering promotion techniques for strawberry export | Applied | Jin-Kyung Kwon | 181.67 | |
Institutes | National Institute of Horticultural and Herbal Science | The effect of root-cutting time on the growth characteristics of strawberries during in situ seeding production | Applied | Jae-Han Lee | 263.33 | |
Institutes | National Institute of Horticultural and Herbal Science | Development of application technology of greenhouse shading agent for stable production in exporting strawberry | Applied | Jae-Han Lee | 100 | |
Institutes | National Institute of Horticultural and Herbal Science | The study of the hanging-bed culture system as a demonstrate culture in greenhouse strawberry | Experimental | Myung-Hwan Cho | 183.33 | |
Institutes | Jeollabuk-do Agricultural Research & Extension Services | The field study of 1st generation smart-farm technology with ICT convergence | Applied | Eun-Ji Kim | 83.33 | |
Miscellaneous | Rural Development Administration | Field demonstration and improvement of growth model of strawberry and tomato for optimal control in a smart greenhouse in Jeonbuk province | Applied | Hye-Jin Lee | 125 |
Region | Type of Organization | Organization | R&D Title | R&D Spectrum | Project Manager | Funding (Thousand USD) |
---|---|---|---|---|---|---|
Jeollabuk-do | University | Jeonbuk National University | Strawberry disease diagnosis web UI advancement and expert utilization system establishment | Experimental | Jun-Hwan Lee | 133.33 |
Jeollabuk-do | Institutes | National Institute of Horticultural and Herbal Science | Image collection and DB upgrade for strawberry disease diagnosis AI training | Experimental | Jong-Han Park | 33.33 |
Chungcheongnam-do | Institutes | Chungcheongnam-do Agricultural Research& Extension Services | Development of control technique of disease and insect pest in hydroponic culture | Applied | Myung-Hyun Nam | 158.33 |
Jeollabuk-do | Institutes | National Institute of Agricultural Sciences | Develop and demonstrate a responsive web UI for strawberry disease based on a cloud system | Experimental | Jeong-Hyun Baek | 41.67 |
Chungcheongnam-do | University | Kongju National University | Development of export strawberry dry damage reduction technology | Experimental | Hyo-Gil Choi | 154.17 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Lee, D.; Kim, K. National Investment Framework for Revitalizing the R&D Collaborative Ecosystem of Sustainable Smart Agriculture. Sustainability 2022, 14, 6452. https://doi.org/10.3390/su14116452
Lee D, Kim K. National Investment Framework for Revitalizing the R&D Collaborative Ecosystem of Sustainable Smart Agriculture. Sustainability. 2022; 14(11):6452. https://doi.org/10.3390/su14116452
Chicago/Turabian StyleLee, Doyeon, and Keunhwan Kim. 2022. "National Investment Framework for Revitalizing the R&D Collaborative Ecosystem of Sustainable Smart Agriculture" Sustainability 14, no. 11: 6452. https://doi.org/10.3390/su14116452