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Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review.

Nikolaos Peladarinos1, Dimitrios Piromalis1, Vasileios Cheimaras1

  • 1Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, Greece.

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Summary
This summary is machine-generated.

Digital Twins create virtual farm replicas for enhanced decision-making. This technology optimizes irrigation, fertilization, and pest control, boosting farm productivity and sustainability.

Keywords:
3D augmented realityIoTagriculture 4.0cyber-physical systemsdigital twin modeldigital twinsprecision farmingsensorssimulationsmart agriculture-farmingvirtual reality

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Area of Science:

  • Agricultural Technology
  • Computer Science
  • Environmental Science

Background:

  • Digital Twins (DTs) are virtual replicas of physical assets, processes, or systems.
  • DTs simulate and forecast real-world behavior, offering dynamic insights.
  • Smart farming leverages DTs for comprehensive virtual farm replicas.

Purpose of the Study:

  • To provide a comprehensive overview of current research on digital twins in smart farming.
  • To explore the applications, impact, and challenges of DTs in agriculture.
  • To identify future research directions for DTs in the agricultural domain.

Main Methods:

  • Amalgamation of diverse data sources: soil, plant condition, environmental sensors, weather predictions, UAV, and satellite imagery.
  • Development of dynamic and up-to-date visualizations of agricultural domains.
  • Analysis of contemporary research on crop modeling, precision agriculture, and associated technologies.

Main Results:

  • DTs enable informed decisions on irrigation, fertilization, and pest management.
  • Enhanced farm productivity and sustainability through data-driven insights.
  • Identification of key challenges including data privacy, data quality, and integration complexity.

Conclusions:

  • Digital Twins offer significant potential to revolutionize smart farming practices.
  • Addressing challenges is crucial for widespread adoption and maximizing benefits.
  • Future research should focus on advancing DT integration and data management in agriculture.