Pulse Brain · Growing Health Evidence Index
Peer-reviewed

Towards A Framework For Farm Scale Digital Twin

I. Fakeye; Ellen Maas; P. Harris; Bader Oulaid; Chris Baker

ACM/IEEE International Conference on Model Driven Engineering Languages and Systems · 2024

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Summary

Enhancing agricultural productivity while maintaining ecological balance amidst climate change is a looming challenge. The future of resilient farming and food security will depend upon the effectiveness of collecting, interpreting, and acting on data. An agricultural digital twin (DT) can provide a feedback loop which improves both farm management and the computer system which informs it through integrating right-time sensor data, process-based models (PBMs), data-driven models (DDMs), and hybrid approaches. Three demonstrator DTs for farm ecosystems are currently under development, utilizing extensive datasets from three instrumented research farms at the North Wyke Farm Platform in Devon, UK to drive and evaluate the accuracy of models in simulating key agroecosystem processes, such as

Subject
Food security & global nutrition
Source type
Peer-reviewed study
System type
Other
DOI
10.1145/3652620.3688264
Catalogue ID
NRmonp58y5-002
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