Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture

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.

Figures (4)

• Figure 1: Example illustration of DSE for cost-effective multimodal software-hardware platforms with unmanned ground vehicles (UGVs or rovers) and unmanned aerial vehicles (UAVs or drones) for precision agriculture.
• Figure 2: Trade-offs among design aspects in Case Study 2 ($1M, 10 acres): (a) unit cost vs. unit payload (b) total cost vs. total payload, (c) total cost vs. area coverage.
• Figure 3: Comparative evaluation of optimizers in Case Study 1 ($100K, 1 acre): (a) total cost vs. payload, (b) total cost vs. area coverage.
• Figure 4: Our prototype hardware constructed in line with the proposed approach's outputs.