Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture
Pawan Kumar, Hokeun Kim

TL;DR
This paper introduces a cost-aware design space exploration framework for multimodal drone-rover cyber-physical systems in precision agriculture, optimizing coverage, payload, and cost constraints.
Contribution
It presents a novel ILP-based DSE method that systematically balances cost, coverage, and payload for farm-specific CPS configurations.
Findings
Achieves full farm coverage within budget constraints.
Outperforms existing CPS design methods in efficiency.
Maximizes payload while maintaining cost-effectiveness.
Abstract
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.
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Taxonomy
TopicsSmart Agriculture and AI · UAV Applications and Optimization · Robotics and Sensor-Based Localization
