Cyber-Physical System Design Space Exploration for Affordable Precision Agriculture

A new research paper from Pawan Kumar and Hokeun Kim, accepted at the 2026 DATE conference, tackles a major barrier in modern farming: the prohibitive cost of high-tech monitoring systems. Their work presents a formal, AI-driven framework for designing affordable cyber-physical systems (CPS) for precision agriculture. The core innovation is a cost-aware design space exploration (DSE) method that systematically evaluates countless configurations of drone and rover platforms. It integrates real-world constraints like budget, energy, sensor payload, computation, and communication into a single optimization problem.

The framework uses integer linear programming (ILP) combined with SAT-based verification to ensure all technical and financial constraints are strictly met. This allows farmers and system designers to explore trade-offs between total cost, area coverage, and sensing capability. For example, the model can determine whether adding a second drone or using a more expensive sensor on a rover is the most cost-effective way to monitor a 100-acre field. The researchers validated their approach with case studies on both small and large farms, demonstrating that it consistently achieves 100% coverage within a set budget while maximizing payload efficiency, outperforming existing state-of-the-art CPS design methods.