IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i3p100-d1358253.html
   My bibliography  Save this article

Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure

Author

Listed:
  • Yogeswaranathan Kalyani

    (School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Liam Vorster

    (School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Rebecca Whetton

    (School of Biosystems and Food Engineering, University College Dublin, D04 V1W8 Dublin, Ireland)

  • Rem Collier

    (School of Computer Science, University College Dublin, D04 V1W8 Dublin, Ireland)

Abstract

In the last decade, digital twin (DT) technology has received considerable attention across various domains, such as manufacturing, smart healthcare, and smart cities. The digital twin represents a digital representation of a physical entity, object, system, or process. Although it is relatively new in the agricultural domain, it has gained increasing attention recently. Recent reviews of DTs show that this technology has the potential to revolutionise agriculture management and activities. It can also provide numerous benefits to all agricultural stakeholders, including farmers, agronomists, researchers, and others, in terms of making decisions on various agricultural processes. In smart crop farming, DTs help simulate various farming tasks like irrigation, fertilisation, nutrient management, and pest control, as well as access real-time data and guide farmers through ‘what-if’ scenarios. By utilising the latest technologies, such as cloud–fog–edge computing, multi-agent systems, and the semantic web, farmers can access real-time data and analytics. This enables them to make accurate decisions about optimising their processes and improving efficiency. This paper presents a proposed architectural framework for DTs, exploring various potential application scenarios that integrate this architecture. It also analyses the benefits and challenges of implementing this technology in agricultural environments. Additionally, we investigate how cloud–fog–edge computing contributes to developing decentralised, real-time systems essential for effective management and monitoring in agriculture.

Suggested Citation

  • Yogeswaranathan Kalyani & Liam Vorster & Rebecca Whetton & Rem Collier, 2024. "Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure," Future Internet, MDPI, vol. 16(3), pages 1-16, March.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:3:p:100-:d:1358253
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/3/100/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/16/3/100/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Verdouw, Cor & Tekinerdogan, Bedir & Beulens, Adrie & Wolfert, Sjaak, 2021. "Digital twins in smart farming," Agricultural Systems, Elsevier, vol. 189(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Uztürk, Deniz & Büyüközkan, Gülçin, 2022. "Smart Agriculture Technology Evaluation: A Linguistic-based MCDM Methodology," Land, Farm & Agribusiness Management Department 337128, Harper Adams University, Land, Farm & Agribusiness Management Department.
    2. Kaikang Chen & Yanwei Yuan & Bo Zhao & Liming Zhou & Kang Niu & Xin Jin & Shengbo Gao & Ruoshi Li & Hao Guo & Yongjun Zheng, 2023. "Digital Twins and Data-Driven in Plant Factory: An Online Monitoring Method for Vibration Evaluation and Transplanting Quality Analysis," Agriculture, MDPI, vol. 13(6), pages 1-18, May.
    3. Gackstetter, David & von Bloh, Malte & Hannus, Veronika & Meyer, Sebastian T. & Weisser, Wolfgang & Luksch, Claudia & Asseng, Senthold, 2023. "Autonomous field management – An enabler of sustainable future in agriculture," Agricultural Systems, Elsevier, vol. 206(C).
    4. Ahmad Ali Hakam Dani & Suhono Harso Supangkat & Fetty Fitriyanti Lubis & I Gusti Bagus Baskara Nugraha & Rezky Kinanda & Irma Rizkia, 2023. "Development of a Smart City Platform Based on Digital Twin Technology for Monitoring and Supporting Decision-Making," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    5. Elanchezhian Arulmozhi & Nibas Chandra Deb & Niraj Tamrakar & Dae Yeong Kang & Myeong Yong Kang & Junghoo Kook & Jayanta Kumar Basak & Hyeon Tae Kim, 2024. "From Reality to Virtuality: Revolutionizing Livestock Farming Through Digital Twins," Agriculture, MDPI, vol. 14(12), pages 1-22, December.
    6. Mezzour Ghita & Benhadou Siham & Medromi Hicham & Mounaam Amine, 2022. "HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring," Energies, MDPI, vol. 15(15), pages 1-38, July.
    7. Shuyao Li & Wenfu Wu & Yujia Wang & Na Zhang & Fanhui Sun & Feng Jiang & Xiaoshuai Wei, 2023. "Production Data Management of Smart Farming Based on Shili Theory," Agriculture, MDPI, vol. 13(4), pages 1-26, March.
    8. Xuehao Bi & Bo Wen & Wei Zou, 2022. "The Role of Internet Development in China’s Grain Production: Specific Path and Dialectical Perspective," Agriculture, MDPI, vol. 12(3), pages 1-14, March.
    9. Zhang, Chen & Di, Liping & Lin, Li & Li, Hui & Guo, Liying & Yang, Zhengwei & Yu, Eugene G. & Di, Yahui & Yang, Anna, 2022. "Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data," Agricultural Systems, Elsevier, vol. 201(C).
    10. da Silveira, Franco & da Silva, Sabrina Letícia Couto & Machado, Filipe Molinar & Barbedo, Jayme Garcia Arnal & Amaral, Fernando Gonçalves, 2023. "Farmers' perception of the barriers that hinder the implementation of agriculture 4.0," Agricultural Systems, Elsevier, vol. 208(C).
    11. Chiara Bersani & Carmelina Ruggiero & Roberto Sacile & Abdellatif Soussi & Enrico Zero, 2022. "Internet of Things Approaches for Monitoring and Control of Smart Greenhouses in Industry 4.0," Energies, MDPI, vol. 15(10), pages 1-30, May.
    12. Tsega Y. Melesse & Chiara Franciosi & Valentina Di Pasquale & Stefano Riemma, 2023. "Analyzing the Implementation of Digital Twins in the Agri-Food Supply Chain," Logistics, MDPI, vol. 7(2), pages 1-17, June.
    13. Uztürk, Deniz & Büyüközkan, Gülçin, 2022. "Smart Agriculture Technology Evaluation: A Linguistic-based MCDM Methodology," Agri-Tech Economics Papers 337128, Harper Adams University, Land, Farm & Agribusiness Management Department.
    14. Metta, Matteo & Ciliberti, Stefano & Obi, Chinedu & Bartolini, Fabio & Klerkx, Laurens & Brunori, Gianluca, 2022. "An integrated socio-cyber-physical system framework to assess responsible digitalisation in agriculture: A first application with Living Labs in Europe," Agricultural Systems, Elsevier, vol. 203(C).
    15. Asif, Muhammad & Searcy, Cory & Castka, Pavel, 2023. "ESG and Industry 5.0: The role of technologies in enhancing ESG disclosure," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    16. Konstantina Ragazou & Alexandros Garefalakis & Eleni Zafeiriou & Ioannis Passas, 2022. "Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector," Energies, MDPI, vol. 15(9), pages 1-17, April.
    17. Büyüközkan, Gülçin & Uztürk, Deniz, 2022. "A Methodology to Investigate Challenges for Digital Twin Technology in Smart Agriculture," Land, Farm & Agribusiness Management Department 337119, Harper Adams University, Land, Farm & Agribusiness Management Department.
    18. Büyüközkan, Gülçin & Uztürk, Deniz, 2022. "A Methodology to Investigate Challenges for Digital Twin Technology in Smart Agriculture," Agri-Tech Economics Papers 337119, Harper Adams University, Land, Farm & Agribusiness Management Department.
    19. Görkem Giray & Cagatay Catal, 2021. "Design of a Data Management Reference Architecture for Sustainable Agriculture," Sustainability, MDPI, vol. 13(13), pages 1-17, June.
    20. Emin Guresci & Bedir Tekinerdogan & Önder Babur & Qingzhi Liu, 2024. "Feasibility of Low-Code Development Platforms in Precision Agriculture: Opportunities, Challenges, and Future Directions," Land, MDPI, vol. 13(11), pages 1-31, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:3:p:100-:d:1358253. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.