Research Progress on Control Algorithms for Grain Combine Harvesters

Intelligent control algorithms are essential for enhancing combine harvester efficiency and minimizing losses, especially as global food demand rises and labor shortages become more severe. This paper provides a comprehensive overview of the evolutionary progression from single-subsystem control to the current core challenge of multi-system co-optimization. We examine the technological development of the cutter, threshing, scavenging, and motion control systems, highlighting persistent bottlenecks that impede global performance improvements due to parameter coupling and conflicting objectives. This review serves as a reference for future advancements in the field. Future research should focus on lightweight reinforcement learning, hybrid control strategies, multimodal perception, and dynamic optimization frameworks for digital twins to drive technological breakthroughs and practical applications.