National Engineering Research Centre for Agri-Product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China.
Int J Environ Res Public Health. 2023 Feb 25;20(5):4120. doi: 10.3390/ijerph20054120.
Changes in storage environments have a significant impact on grain quality. Accurate prediction of any quality changes during grain storage in different environments is very important for human health. In this paper, we selected wheat and corn, which are among the three major staple grains, as the target grains whose storage monitoring data cover more than 20 regions, and constructed a grain storage process quality change prediction model, which includes a FEDformer-based grain storage process quality change prediction model and a K-means++-based grain storage process quality change grading evaluation model. We select six factors affecting grain quality as input to achieve effective prediction of grain quality. Then, evaluation indexes were defined in this study, and a grading evaluation model of grain storage process quality was constructed using clustering model with the index prediction results and current values. The experimental results showed that the grain storage process quality change prediction model had the highest prediction accuracy and the lowest prediction error compared with other models.
存储环境的变化对粮食质量有重大影响。准确预测不同环境下粮食储存过程中的任何质量变化，对于人类健康非常重要。在本文中，我们选择了小麦和玉米这三种主要粮食作物作为目标粮食，这些粮食的储存监测数据涵盖了 20 多个地区，并构建了一个粮食储存过程质量变化预测模型，该模型包括基于 FEDformer 的粮食储存过程质量变化预测模型和基于 K-means++的粮食储存过程质量变化分级评估模型。我们选择了六个影响粮食质量的因素作为输入，以实现对粮食质量的有效预测。然后，本研究定义了评价指标，并利用聚类模型的指标预测结果和当前值构建了粮食储存过程质量的分级评价模型。实验结果表明，与其他模型相比，粮食储存过程质量变化预测模型具有最高的预测精度和最低的预测误差。
