Li Xinze, Wu Wenfu, Guo Hongpeng, Wu Yunshandan, Li Shuyao, Wang Wenyue, Lu Yanhui
College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
Institute of XinJiang Grain and Oil Science, Urumqi 830000, China.
Foods. 2025 Mar 18;14(6):1024. doi: 10.3390/foods14061024.
In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach based on a deep learning framework for grain storage state monitoring and risk alert. This approach combines two advanced three-dimensional deep learning models, a grain storage state classification model based on 3D DenseNet and a temperature field prediction model based on 3DCNN-LSTM. First, the grain storage state classification model based on 3D DenseNet efficiently extracts features from three-dimensional grain temperature data to achieve the accurate classification of storage states. Second, the temperature prediction model based on 3DCNN-LSTM incorporates historical grain temperature and absolute water potential data to precisely predict the dynamic changes in the granary's temperature field. Finally, the grain temperature prediction results are input into the 3D DenseNet to provide early warnings for potential condensation and mildew risks within the grain pile. Comparative experiments with multiple baseline models show that the 3D DenseNet model achieves an accuracy of 97.38% in the grain storage state classification task, significantly outperforming other models. The 3DCNN-LSTM model shows high prediction accuracy in temperature forecasting, with MAE of 0.24 °C and RMSE of 0.28 °C. Furthermore, in potential risk alert experiments, the model effectively captures the temperature trend in the grain storage environment and provides early warnings, particularly for mildew and condensation risks, demonstrating the potential of this method for grain storage safety monitoring and risk alerting. This study provides a smart grain storage solution which contributes to ensuring food safety and enhancing the efficiency of grain storage management.
为了克服当前粮食储存状态监测方法的显著局限性，特别是在潜在风险预警和粮仓内粮食温度空间分布分析方面，本研究提出了一种基于深度学习框架的多模型融合方法，用于粮食储存状态监测和风险预警。该方法结合了两个先进的三维深度学习模型，一个基于3D DenseNet的粮食储存状态分类模型和一个基于3DCNN-LSTM的温度场预测模型。首先，基于3D DenseNet的粮食储存状态分类模型从三维粮食温度数据中高效提取特征，以实现储存状态的准确分类。其次，基于3DCNN-LSTM的温度预测模型结合历史粮食温度和绝对水势数据，精确预测粮仓温度场的动态变化。最后，将粮食温度预测结果输入到3D DenseNet中，为粮堆内潜在的结露和霉变风险提供预警。与多个基线模型的对比实验表明，3D DenseNet模型在粮食储存状态分类任务中准确率达到97.38%，显著优于其他模型。3DCNN-LSTM模型在温度预测方面显示出较高的预测精度，平均绝对误差为0.24°C，均方根误差为0.28°C。此外，在潜在风险预警实验中，该模型有效地捕捉了粮食储存环境中的温度趋势并提供预警，特别是对于霉变和结露风险，证明了该方法在粮食储存安全监测和风险预警方面的潜力。本研究提供了一种智能粮食储存解决方案，有助于确保食品安全并提高粮食储存管理效率。
