Summary
This paper presents a Multi-Objective Causal Decision-Making Framework using edge-cloud collaborative digital twins to address technical and agronomic challenges in precision agriculture. The system combines lightweight edge-deployed feature extraction with a cloud-based causal reasoning engine to simulate crop growth trajectories and optimise control decisions balancing pest management, crop vigour, and environmental stewardship. The framework demonstrates substantial reductions in data transmission overhead whilst maintaining crop quality and ecological objectives.
Regional applicability
The study geography is not specified in the abstract. The framework's applicability to United Kingdom farming would depend on validation with local crop varieties, pest pressures, and climate conditions. The edge-cloud architecture and bandwidth-reduction approach are platform-agnostic and could be transferable, but field validation with UK-relevant crops and environmental conditions would be necessary.
Key measures
Bandwidth reduction ratio, flower bud preservation percentage, Eco-Health score, multi-objective optimisation outcomes for pest suppression efficiency and ecological sustainability
Outcomes reported
The study evaluated a multi-objective causal decision-making framework that optimises pest suppression, crop health, and ecological sustainability simultaneously. The framework reduced bandwidth consumption by over 2000 times whilst preserving over 85% of flower buds and maintaining an Eco-Health score above 90.
Topic tags
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