Grain quality preservation: an integrated review of deterioration mechanisms, detection technologies, and mitigation strategies

Postharvest grain deterioration threatens global food security and economic stability, arising from complex interactions between intrinsic grain properties and environmental stressors. Despite advances in detection and control technologies, critical bottlenecks persist, including insufficient understanding of multi-factor synergies, limited scalability for smallholders, and the lack of integrated predictive management frameworks. This review systematically analyzes these gaps by constructing a novel framework centered on "Environmental drivers, biological responses, and technological interventions." It critically synthesizes deterioration mechanisms, provides a decision-oriented comparative evaluation of detection and control technologies based on performance, cost, scalability, and user scenarios, and identifies key research voids in modeling, generalizability, and climate resilience. Future breakthroughs hinge on interdisciplinary innovation, prioritizing: predictive digital twin systems integrating storage physics, biology, and quality models; intelligent responsive green materials and low-energy storage designs; and standardized data ecosystems with blockchain traceability to incentivize quality preservation. This review provides three integrated outputs: a qualitative framework linking environmental drivers, responses, and interventions; a scenario-specific decision matrix comparing technologies by performance, cost, and scalability; and a research roadmap prioritizing digital twins, green materials, and data ecosystems. The framework aims to translate mechanistic insights into practical management decisions and guide grain storage toward intelligent, resilient, and sustainable systems.