ArticleInternational journal of environmental research and public health2023
A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Influence of Interannual Climate Variability on Wheat Quality During Long-Term Storage: Comparative Analysis and Mathematical Modeling.Foods (Basel, Switzerland) · 2026Article
- Climate Change and Aflatoxin BFood science & nutrition · 2026Review
- Year-independent prediction of rice grain protein content using machine learning with agronomy-aligned multi-year field data.Frontiers in plant science · 2026Article
- Curcumin Attenuates Zearalenone-Induced Reproductive Damage in Mice by Modulating the Gut Microbe-Testis Axis.Foods (Basel, Switzerland) · 2025Article
- Total Deoxynivalenol Contamination of Wheat Products and Coarse Grains in Shanghai, China: Occurrence and Health Risk Assessment.Foods (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
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.
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Registered trials
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