智能谷物存储解决方案:谷物存储监测和风险警报的集成深度学习框架
Xinze Li1, Wenfu Wu1, Hongpeng Guo1
1College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
Foods (Basel, Switzerland)
|April 15, 2025
概括
本研究引入了用于智能谷物存储的深度学习方法,增强了早期风险检测和温度监测. 先进的模型提高了谷物安全和管理效率.
科学领域:
- 农业工程 农业工程
- 人工智能的人工智能
- 食品科学 食品科学 食品科学
背景情况:
- 目前的谷物储存监测方法在早期风险检测和空间温度分析方面存在局限性.
- 精确的监测对于防止腐烂和确保食品安全至关重要.
研究的目的:
- 开发一个多模型融合深度学习框架,用于谷物储存状态监测和风险预警.
- 提高谷物储存状态分类和温度场预测的准确性.
- 为了提供对凝结和风险的早期警告.
主要方法:
- 使用3D谷物温度数据使用3DDDenseNet模型进行谷物储存状态分类.
- 采用3DCNN-LSTM模型,用历史温度和水潜力数据预测动态温度场变化.
- 综合预测结果用于风险警报的分类模型.
主要成果:
- 3D DenseNet在谷物存储状态分类中实现了97.38%的准确性.
- 3DCNN-LSTM显示了高温预测的准确性 (MAE:0.24°C,RMSE:0.28°C).
- 综合模型有效地为菌和凝结风险提供了早期警告.
结论:
- 拟议的深度学习框架为谷物存储安全提供了一个智能解决方案.
- 这种方法提高了食品安全和谷物储存管理效率.
- 多模型融合方法显示了实时监控和谷仓风险预警的巨大潜力.
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