AI-Driven Cooperative Control for Autonomous Tractors and Implements: A Comprehensive Review

Artificial intelligence (AI) is driving the evolution of autonomous agriculture towards multi-agent collaborative control, breaking through the limitations of traditional isolated automation. Although existing research has focused on hierarchical control and perception-decision-making technologies for agricultural machinery, the overall integration of these elements in building a resilient physical perception collaborative system is still insufficient. This paper systematically reviews the progress of AI-driven tractor-implement cooperative control from 2018 to 2025, focusing on four major technical pillars: (1) perception-decision-execution hierarchical architecture, (2) distributed multi-agent collaborative framework, (3) physical perception modeling and adaptive control, and (4) staged operation applications (such as collaborative harvesting). The research reveals core challenges such as real-time collaborative planning, perception robustness under environmental disturbances, and collaborative control and safety assurance under operational disturbances. To this end, three solutions are proposed: an AI framework for formalizing agronomic constraints and mechanical dynamics; a disturbance-resistant adaptive tractor-implement cooperative control strategy; and a real-time collaborative ecosystem integrating neuromorphic computing and FarmOS. Finally, a research roadmap is summarized with agronomic constraint reinforcement learning, self-reconfigurable collaboration, and biomechanical mechatronic systems as the core. By integrating the scattered progress in AI, robotics and agronomy, we provide theoretical foundation and practical guidance for scalable and sustainable autonomous farm systems.