Scholarly record
MODELLING AND SIMULATION OF CLOUD-BASED DIGITAL TWINS IN SMART FARMING
Abstract
Digital Twins can be seen as powering the next generation of IoT-connected solutions. Digital Twins model the real world by using historical and real-time data to represent the past and present and simulate the predictable future. Digital twins are related to a set of concepts such as digital representation and 3D visualization, integration, monitoring, control, computation, prediction, and decision-making. They are digital replicas of physical objects having bidirectional data flow. The physical object and its digital twin are synchronized, and the simulations, optimizations and visualizations are in real-time. Using Digital Twins supports the processes of gaining insights that drive better products, optimize operations, reduce costs, and improve the customer experience. These benefits can be used in any type of environment, including buildings, factories, farms, power grids, and even entire cities. Data gathered as a result of the implementation of Precision Livestock Farming (PLF) techniques allows the creation of digital twins though out the farm. As a result, farmers can manage the farm remotely based on real-time digital information, rather than relying on direct observation and manual tasks on the ground. This allows them to act immediately in case of deviations, simulate the effect of interventions based on real-life data and automate various decision-making processes. The main goal of the article is modelling and simulations of digital twins for smart farming in a Cloud environment. During operational use, digital twins can be used not only to monitor and simulate the effects of interventions but also to remotely control objects by using automated actuators. Finally, digital twins are also very valuable for traceability, compliance, and training as they optimize farm operations and provide measurable data for increasing sustainability.
Publication Impact Profile
- Citations
- Scopus - Citation Indexes: 3
- Captures
- Mendeley - Readers: 21
Publication details
References11
Sepasgozar, S.M.E. Digital Twin and Cities. In: The Palgrave Encyclopedia of Urban and Regional Futures. Palgrave Macmillan, Springer, pp. 1 - 6, 2022. DOI: 10.1007/978-3-030-51812-7_253-1
Jouanny C., Vidal Ch., Capgemini perspective: Digital twins. Mirroring the real world for a better and sustainable performance. Intelligent Industry, Capgemini research institute, 3, 2021.
Alves G., Souza G., Maia R. F., Tran A. L. H., Kamienski C., Soininen J.-P., Aquino P. T., Lima F., A digital twin for smart farming, 2019 IEEE GHTC, pp. 1-4, 2019. DOI: 10.1109/ghtc46095.2019.9033075
Shaikh T.A., Rasool T., Lone F.R., Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming, Computers and Electronics in Agriculture, vol. 198, 107119, 2022. DOI: 10.1016/j.compag.2022.107119
Skobelev P., Laryukhin V., Simonova E., Goryanin O., Yalovenko V., and Yalovenko O., Developing a smart cyber-physical system based on digital twins of plants, 2020 Fourth WorldS4, pp. 522-527, 2020. DOI: 10.1109/worlds450073.2020.9210359
Neethirajan S., Kemp B., Digital Twins in Livestock Farming, Animals, vol. 11, issue 4, 1008, 2021. DOI: 10.3390/ani11041008
Verdouw C., Tekinerdogan B., Beulens A., Wolfert S., Digital twins in smart farming, Agricultural Systems, vol. 189, 103046, 2021. DOI: 10.1016/j.agsy.2020.103046
Guzina L., Ferko E., Alessio Bucaioni A., Investigating Digital Twin: A Systematic Mapping Study, SPS22: Proceedings of the 10th Swedish Production Symposium, 2022. DOI: 10.3233/atde220164
Dineva K., Atanasova T., Cloud Data-Driven Intelligent Monitoring System for Interactive Smart Farming, Sensors, vol. 22, issue 17, 6566, 2022. DOI: 10.3390/s22176566
Chikurtev D., Ivanov V., Yosifova V., Dimitrov D., Cyber-physical system for intelligent control of infrared heating. IFAC, vol.55, issue 11, pp. 37-41, 2022 DOI: 10.1016/j.ifacol.2022.08.045
Barnes, J. Microsoft Azure Essentials Azure Machine Learning; Microsoft Press: Unterschleissheim, Germany, 2015
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.