Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture

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.