Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture
This addresses the problem of making precision agriculture more affordable for farmers, though it is incremental as it builds on existing CPS design methods.
The paper tackles the high cost and lack of systematic design methods in precision agriculture by presenting a cost-aware design space exploration framework for drone-rover platforms, achieving full coverage within budget and maximizing payload efficiency in case studies.
Precision agriculture promises higher yields and sustainability, but adoption is slowed by the high cost of cyber-physical systems (CPS) and the lack of systematic design methods. We present a cost-aware design space exploration (DSE) framework for multimodal drone-rover platforms to integrate budget, energy, sensing, payload, computation, and communication constraints. Using integer linear programming (ILP) with SAT-based verification, our approach trades off among cost, coverage, and payload while ensuring constraint compliance and a multitude of alternatives. We conduct case studies on smaller and larger-sized farms to show that our method consistently achieves full coverage within budget while maximizing payload efficiency, outperforming state-of-the-art CPS DSE approaches.