A digital twin for smart farming

Alves, Rafael Gomes; Souza, Gilberto Rostirolla Batista de; Maia, Rodrigo Filev; Tran, Anh; Kamienski, Carlos; Soininen, Juha-Pekka; Aquino, Plínio Thomaz; Lima, Fábio

doi:10.1109/ghtc46095.2019.9033075

Cited by 83 publications

(72 citation statements)

References 15 publications

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“…[68]. Due to recent technological advancements, the agriculture sector has also incorporated DT into crop management, disease prediction, the evaluation of fertilizers through data captured from the field (temperature, humidity, soil moisture, light intensity, pressure) and plant image data [69,70].…”

Section: Application Domains Of Digital Twinmentioning

confidence: 99%

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

et al. 2021

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The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

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Section: Application Domains Of Digital Twinmentioning

confidence: 99%

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

et al. 2021

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“…We have selected the studies that explicitly discuss the design of digital twins and that were pioneering. The selected list of primary studies that we used to analyze the concept of digital twins and, in particular, the design of digital twins are as follows: [ 1 , 14 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Domain Analysis Of Digital Twinsmentioning

confidence: 99%

Systems Architecture Design Pattern Catalog for Developing Digital Twins

Tekinerdoğan

Verdouw

2020

Sensors

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A digital twin is a digital replica of a physical entity to which it is remotely connected. A digital twin can provide a rich representation of the corresponding physical entity and enables sophisticated control for various purposes. Although the concept of the digital twin is largely known, designing digital twins based systems has not yet been fully explored. In practice, digital twins can be applied in different ways leading to different architectural designs. To guide the architecture design process, we provide a pattern-oriented approach for architecting digital twin-based Internet of Things (IoT) systems. To this end, we propose a catalog of digital twin architecture design patterns that can be reused in the broad context of systems engineering. The patterns are described using the well-known documentation template and support the various phases in the systems engineering life cycle process. For illustrating the application of digital twin patterns, we adopt a case study in the agriculture and food domain.

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“…To be able to run simulations and explore system behaviour in regions of the state space that have previously not been reached, the digital twin does not only need a representation of the sensor data and system states, as for example shown in [11], but also an AI component for the decision making process of each agent, including the communication between agents.…”

Section: The Digital Twin As a Multi-agent Systemmentioning

confidence: 99%

“…A second dimension of the digital twin is leveraging sensor data of the agricultural processes and environmental conditions to improve the yield and resource usage, as well as observing the behaviour and health of plants and livestock, remotely and supported by AI [11].…”

Section: Introductionmentioning

confidence: 99%

A Digital Twin of the Social-Ecological System Urban Beekeeping

Johannsen¹,

Senger²,

Kluß³

2020

Progress in IS

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We describe the system design and setup of our digital twin of the socialecological system urban beekeeping, with the aim to support agroecological methods in urban agriculture. The physical space consists of the bee populations, their beekeepers who are part of a beekeeping community, non-beekeepers who consume honey, organisational actors shaping rules and regulations and the environment. The virtual space is a multi-agent model, where autonomous agents can take actions and make decisions in partially observed Markov processes. To tie the physical and the virtual space, we embedded bee hives in an IoT environment and implemented an online documentation tool as a web application, where beekeepers take short notes about their work and observations. Bee hives are equipped with sensors, such as humidity, pressure and temperature sensors and a scale. Additionally, we pull data from the German weather service (Deutscher Wetter Dienst, DWD). In our system architecture, multiple levels on data fusion are performed, beginning with raw data quality estimation and sensor failure detection. On higher levels, states of entities are estimated, such as the health of a bee colony, and assessment made whether a state is normal or to be considered an anomaly. Finally on the highest level, we deal with the desires of our agents, how actions should be chosen in order to achieve or maintain desirable and rewarding world states. We hope to be able to refine our digital twin into a decision support tool for small-scale (bee) farmers and communal political actors that helps to reach desirable world states by predicting and simulating the effects of actions within the complex system of urban beekeeping.

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A digital twin for smart farming

Cited by 83 publications

References 15 publications

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

Systems Architecture Design Pattern Catalog for Developing Digital Twins

A Digital Twin of the Social-Ecological System Urban Beekeeping

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