Digital twin deployment for smart agriculture in Cloud-Fog-Edge infrastructure

Kalyani, Yogeswaranathan; Bermeo, Nestor Velasco; Collier, Rem W.

doi:10.1080/17445760.2023.2235653

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…Simulated datasets have also been used [68,99] to enhance DTs by providing controlled training environments, filling data gaps, and enabling learning from hypothetical scenarios. Sonic devices were utilized in one study for the detection of animal intrusion [80], while NDVI (Normalized Difference Vegetation Index) sensors in [82] provided crucial insights into crop health. Finally, the utilization of impact and rotation sensors was described in [67] for simulating the impacts the potatoes face during harvest.…”

Section: Summary Of the Main Physical Assets Data Acquisition Methods...mentioning

confidence: 99%

“…DTs play a fundamental role in crop monitoring, offering real-time insights into crop conditions through the integration of data from diverse sources such as inter alia, cameraequipped UAVs [70,74,75,77,85,86] and UGVs [79], satellites [77,83], soil sensors [70,75,81,83], weather stations [74,75,[81][82][83], and knowledge databases [66,[71][72][73][74]77,82,84]. This integration allows for comprehensive analyses, aiding farmers in optimizing critical open-field agricultural practices like irrigation, fertilization, and pest control.…”

Section: Rq1: How Are Dts Conceptualized and Defined In Agricultural ...mentioning

confidence: 99%

“…By simulating the potential impact of factors, such as weather patterns, soil conditions, and resource management strategies, farmers can make informed decisions to enhance crop growth and yield. In summary, the multifaceted capabilities of DTs indicatively include resource rescheduling [72][73][74], growth monitoring and prediction [73,74,82,84], yield prediction [83][84][85], scene reconstruction [69], path planning [86], and addressing challenges like disease detection [70] and personnel and finance management [74].…”

Section: Rq1: How Are Dts Conceptualized and Defined In Agricultural ...mentioning

confidence: 99%

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Digital Twins in Agriculture and Forestry: A Review

Tagarakis,

Benos,

Kyriakarakos

et al. 2024

Sensors

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Digital twins aim to optimize practices implemented in various sectors by bridging the gap between the physical and digital worlds. Focusing on open-field agriculture, livestock farming, and forestry and reviewing the current applications in these domains, this paper reveals the multifaceted roles of digital twins. Diverse key aspects are examined, including digital twin integration and maturity level, means of data acquisition, technological capabilities, and commonly used input and output features. Through the prism of four primary research questions, the state of the art of digital twins, the extent of their achieved integration, and an overview of the critical issues and potential advancements are provided in the landscape of the sectors under consideration. The paper concludes that in spite of the remarkable progress, there is a long way towards achieving full digital twin. Challenges still persist, while the key factor seems to be the integration of expert knowledge from different stakeholders. In light of the constraints identified in the review analysis, a new sector-specific definition for digital twins is also suggested to align with the distinctive characteristics of intricate biotic and abiotic systems. This research is anticipated to serve as a useful reference for stakeholders, enhancing awareness of the considerable benefits associated with digital twins and promoting a more systematic and comprehensive exploration of this transformative topic.

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Section: Summary Of the Main Physical Assets Data Acquisition Methods...mentioning

confidence: 99%

Section: Rq1: How Are Dts Conceptualized and Defined In Agricultural ...mentioning

confidence: 99%

Section: Rq1: How Are Dts Conceptualized and Defined In Agricultural ...mentioning

confidence: 99%

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Digital Twins in Agriculture and Forestry: A Review

Tagarakis,

Benos,

Kyriakarakos

et al. 2024

Sensors

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“…Edge computing, situated closer to on-field devices, ensures fast data processing and minimal latency, which is essential for real-time decision-making [32]. However, fog computing plays a pivotal role in this integration, not only seamlessly connecting with edge devices but also conducting intelligent analysis [33,34]. The unique capability of fog computing to extend its reach to both the edge and the cloud makes it an indispensable component of agricultural systems.…”

Section: Cloud-fog-edge Computingmentioning

confidence: 99%

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

Kalyani,

Vorster,

Whetton

et al. 2024

Future Internet

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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.

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