Zhang Qingchuan, Song Zexi, Bi Mingwen
National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, No. 11 and No. 33, Fucheng Road, Haidian District, Beijing 100048, China.
Business School, Beijing Wuzi University, No. 321, Fuhe Street, Tongzhou District, Beijing 101149, China.
Foods. 2025 May 12;14(10):1715. doi: 10.3390/foods14101715.
Wheat is a vital staple food crop, and its post-harvest storage is paramount to maintaining its quality. However, conventional grain storage methods frequently impede the ability to promptly and accurately predict and assess quality changes. Moreover, most storage systems are ineffective in dealing with the impact of temperature and humidity fluctuations on wheat quality, which can potentially lead to quality degradation during storage. To address these challenges, this paper proposes a dual model system of "prediction-evaluation", which integrates a dynamic quality prediction model based on SGCNiFormer with an evaluation framework based on K-Smeans clustering to establish a closed-loop mechanism from quality prediction to storage effect evaluation. The system incorporates a graph convolutional network (GCN) and a dynamic gating module, enabling precise simulation of the multidimensional evolution of wheat quality under the interaction of moisture and temperature. The experimental results demonstrate the superiority of SGCNiFormer in time-series prediction tasks, while the K-Smeans method establishes a wheat quality grading standard with physical interpretability. This integrated method provides a systematic theoretical framework for optimizing storage parameters and offers substantial support for intelligent grain storage management.
小麦是一种至关重要的主食作物，其收获后的储存对于保持其品质至关重要。然而，传统的谷物储存方法常常阻碍及时、准确地预测和评估品质变化的能力。此外，大多数储存系统在应对温度和湿度波动对小麦品质的影响方面效果不佳，这可能导致储存期间品质下降。为应对这些挑战，本文提出了一种“预测-评估”双模型系统，该系统将基于SGCNiFormer的动态品质预测模型与基于K-Smeans聚类的评估框架相结合，以建立从品质预测到储存效果评估的闭环机制。该系统包含一个图卷积网络（GCN）和一个动态门控模块，能够在水分和温度相互作用下精确模拟小麦品质的多维演变。实验结果证明了SGCNiFormer在时间序列预测任务中的优越性，而K-Smeans方法建立了具有物理可解释性的小麦品质分级标准。这种集成方法为优化储存参数提供了系统的理论框架，并为智能粮食储存管理提供了有力支持。
