Digital twins in smart farming
By Cor N. Verdouw, Bedir Tekinerdogan, Adrie J. M. Beulens, Sjaak Wolfert. Agricultural Systems, 2021
551 citations recorded by the source corpus.
Abstract Digital Twins are very promising to bring smart farming to new levels of farming productivity and sustainability. A Digital Twin is a digital equivalent of a real-life object of which it mirrors its behaviour and states over its lifetime in a virtual space. Using Digital Twins as a central means for farm management enables the decoupling of physical flows from its planning and control. As a consequence, farmers can manage operations remotely based on (near) real-time digital information instead of having to rely on direct observation and manual tasks on-site. This allows them to act immediately in case of (expected) deviations and to simulate effects of interventions based on real-life data. This paper analyses how Digital Twins can advance smart farming. It defines the concept, develops a typology of different types of Digital Twins, and proposes a conceptual framework for designing and implementing Digital Twins. The framework comprises a control model based on a general systems approach and an implementation model for Digital Twin systems based on the Internet of Things—Architecture (IoT-A), a reference architecture for IoT systems. The framework is applied to and validated in five smart farming use cases of the European IoF2020 project, focussing on arable farming, dairy farming, greenhouse horticulture, organic vegetable farming and livestock farming.
Subfields: Computer Networks and Communications, Industrial and Manufacturing Engineering, Plant Science
View Paper: https://www.semanticscholar.org/paper/96de9ebbfc453d52c255d90d55eb7d09206c9581
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In Industrial and Manufacturing Engineering
In Plant Science
Citations in the corpus
- Digital transformation of agriculture and rural areas: A socio-cyber-physical system framework to support responsibilisation (2021, background): “Through for example digital twins, virtual replications of physical systems continuously updated by their twins ’ data (El Saddik, 2018; Pylianidis et al., 2021; Verdouw et al., 2021)), it is possible to predict harmful events in a physical system and intervene before the events occur.”