Research Progress on Control Algorithms for Grain Combine Harvesters
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Advanced Algorithms and Applications · Belt Conveyor Systems Engineering · Agricultural Engineering and Mechanization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Nanjing Institute of Agricultural Mechanisation, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
Nanjing Institute of Technology, College of Mechanical Engineering, Nanjing 211167, China
Funding
CAAS Center for Science in Smart Agriculture and Equipment · Central-level public Fund for Basic Scientific Research of the Chinese Academy of Agricultural Sciences · National Natural Science Foundation of China
References (100+)
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