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Review
. 2024 May 15;72(19):10737-10752.
doi: 10.1021/acs.jafc.4c01934. Epub 2024 May 6.

Digital Twins in Agriculture: Orchestration and Applications

Marc Escribà-Gelonch  1 , Shu Liang  1   2   3 , Pieter van Schalkwyk  4 , Ian Fisk  5   6 , Nguyen Van Duc Long  2   3   7 , Volker Hessel  2   3   7
Affiliations
Review

Digital Twins in Agriculture: Orchestration and Applications

Marc Escribà-Gelonch et al. J Agric Food Chem. .

Abstract

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

Keywords: Digital Twins; Industry 4.0; Internet of Things; agriculture 4.0; precise agriculture.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Technological evolution of agriculture along ages.
Figure 2
Figure 2
General structure of the IoT platform.
Figure 3
Figure 3
Data flow: data collection, preprocessing, modeling, simulation, analytics, decision support, visualization, and control, ultimately aiding in optimizing farm operations and improving decision-making processes.
Figure 4
Figure 4
Common machine learning data flow in digital twins.
Figure 5
Figure 5
Digital twin interaction along product lifecycle.
Figure 6
Figure 6
Conceptual structure of multiple DT organizations along lifecycle.
Figure 7
Figure 7
Sustainable benefits and adverse effects of using digital twins in agriculture.
Figure 8
Figure 8
Possible applications of digital twins along the supply chain in agriculture.
Figure 9
Figure 9
Environment-aware digital twin scheme with weather and climate information.
Figure 10
Figure 10
Digital twin architecture for agricultural production.
Figure 11
Figure 11
Digital twin architecture in postharvest and food processing optimization.
Figure 12
Figure 12
Actors involved in a DT architecture (arrows are data flow).
Figure 13
Figure 13
Cloud functions as a data source for AI.
Figure 14
Figure 14
General scheme of hydroponic system parts suitable to be managed by digital twins.
Figure 15
Figure 15
Aquaponic system parts suitable to be managed by digital twins.
Figure 16
Figure 16
SWOT matrix of digital twin application in agriculture.
See this image and copyright information in PMC

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