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August 26, 2026Agronomy0 citationsOpen Access

Research on Material Conveying and Collection Technology During Crop Harvesting

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SJSentao JiangJBJing BaiHFHuimin Fang

Key Points

  • To comprehensively evaluate material conveying and collection technologies, physical simulation methods, and intelligent control strategies to establish a system-level optimization framework for crop harvesting.
  • Analyzed screw, clamping-flexible, chain/vibrating, and pneumatic conveying systems across performance metrics including efficiency, crop damage, energy consumption, and adaptability.
  • Evaluated numerical simulation frameworks (DEM, dynamic/vibro-acoustic analysis, CFD–DEM) and intelligent control architectures (multi-source sensing, hybrid modeling, feedforward–feedback control).
  • Demonstrated that no single conveying technology is universally optimal, requiring selection based on crop physical properties, field conditions, and primary operational targets.
  • Identified critical computational trade-offs between physical fidelity and calculation cost across discrete element and coupled CFD–DEM simulation frameworks.
  • Proposed a unified system-level framework combining property-informed mechanism selection, hybrid modeling, and adaptive closed-loop control to minimize grain damage and energy use.

Abstract

Material conveying and collection are critical to harvesting efficiency, crop quality, energy consumption, and operational continuity in combine harvesters. However, existing studies mainly focus on individual technologies, while systematic criteria for technology comparison and selection remain insufficient. This review critically analyzes major conveying and collection technologies, mechanism-based simulation methods, and intelligent sensing and control strategies. Screw, clamping-flexible, chain/vibrating, and pneumatic conveying systems are compared in terms of conveying efficiency, crop damage and material loss, energy consumption, reliability, and adaptability. DEM, dynamic/vibro-acoustic analysis, and CFD–DEM are further evaluated according to their applicable mechanisms, physical fidelity, and computational cost. Recent advances in multi-source sensing, data-driven prediction, and feedforward–feedback control are summarized. Based on these comparisons, a system-level optimization framework is proposed, emphasizing efficiency, quality preservation, and energy efficiency while maintaining operational reliability and adaptability. The review indicates that no single technology is universally optimal; technology selection should be matched to crop properties, operating conditions, and dominant performance objectives. Future research should focus on material-property-informed technology selection, mechanism–data hybrid modeling, and adaptive closed-loop control for intelligent harvesting systems.

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Cite This Study

Jiang et al. (2026) studied this question.

synapsesocial.com/papers/6a8e9af2451774b83f3b37c0https://doi.org/10.3390/agronomy16171626
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