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Digital Twin for Predictive Monitoring of Crops: State of the Art

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Proceedings of Eighth International Congress on Information and Communication Technology (ICICT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 695))

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Abstract

Recently, the use of digital twins in crop management has caught the attention of the agricultural sector. This technology is still in its early phases of deployment, and the state-of-the-art methodologies and adoption level of digital twins have not been thoroughly explored. To address this issue, this paper discusses the current trend of crop predictive monitoring using digital twin applications, focusing on the approaches used, adoption levels, and implementation challenges. Digital twins in crop management are still in the lab stage, and large-scale implementations in farming are not reported. Despite the benefits of increased crop productivity, the adoption of digital twins is hampered by challenges such as the complexity of modeling, poor high-speed Internet connectivity in rural areas, data security, significant investment costs, data accuracy, and a lack of knowledge about crop types and farming circumstances. Insights are provided to research academics, companies, and practitioners to help them understand the current state-of-the-art problems and future research prospects in the sector.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, University of Salerno, Via G. Paolo II, Fisciano, SA, Italy

    Tsega Y. Melesse, Francesco Colace, Angelo Lorusso, Domenico Santaniello & Carmine Valentino

  2. University of Tartu, Ulikooli 18, Tartu, 50090, Estonia

    Simon Pierre Dembele

Authors
  1. Tsega Y. Melesse
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  2. Francesco Colace
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  3. Simon Pierre Dembele
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  4. Angelo Lorusso
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  5. Domenico Santaniello
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  6. Carmine Valentino
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Corresponding author

Correspondence to Tsega Y. Melesse .

Editor information

Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University London, London, UK

    Xin-She Yang

  2. Department of Biomedical Engineering, University of Reading, Reading, UK

    R. Simon Sherratt

  3. Department of Computer Science and Engineering, Techno International New Town, Chakpachuria, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Melesse, T.Y., Colace, F., Dembele, S.P., Lorusso, A., Santaniello, D., Valentino, C. (2024). Digital Twin for Predictive Monitoring of Crops: State of the Art. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 695. Springer, Singapore. https://doi.org/10.1007/978-981-99-3043-2_85

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  • DOI: https://doi.org/10.1007/978-981-99-3043-2_85

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3042-5

  • Online ISBN: 978-981-99-3043-2

  • eBook Packages: EngineeringEngineering (R0)

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