Early Detection and Management of Crop Pests Using Quadruped Robotic Systems: Challenges and Open Problems: Invited Position Paper

Early detection and mitigation of crop pests like the Fall Armyworm is critical to ensuring food security, particularly for pest-sensitive crops such as maize and sorghum. Traditional monitoring approaches based on manual scouting or centralized analytics struggle to meet the high-precision real-time requirements of pest outbreaks, especially in remote and resource-constrained agricultural settings. In this paper, we present the design vision of AgriCane, a cyber-physical system (CPS) architecture that integrates legged robotic platforms, edge-native machine learning pipelines, and low-bandwidth communication to form a closed sensing-compute-actuation loop. Our vision is to have quadruped robots equipped with manipulators for fine-grained inspection under leaves and at soil level, performing lightweight, on-device detection and escalating uncertain cases to cloud-based models via compressed, opportunistic backhaul. This paper outlines the architectural pillars of AgriCane and identifies key open challenges across the communication, computing, and control stack, ranging from uncertainty-aware inference and real-time robotic control to hierarchical decision pipelines and adaptive scouting. Our goal is to foster cross-disciplinary discussion on the design of resilient, autonomous CPS for openfield agriculture.