College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.
Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China.
Sensors (Basel). 2024 Oct 11;24(20):6556. doi: 10.3390/s24206556.
Mildew infestation is a significant cause of loss during grain storage. The growth and metabolism of mildew leads to changes in gas composition and temperature within granaries. Recent advances in sensor technology and machine learning enable the prediction of grain mildew during storage. Current research primarily focuses on predicting mildew occurrence or grading using simple machine learning methods, without in-depth exploration of the time series characteristics of mildew process data. A monitoring device was designed and developed to capture high-quality microenvironment parameters and image data during a simulated mildew process experiment. Using the "Yongyou 15" rice varieties from Zhejiang Province, five simulation experiments were conducted under varying temperature and humidity conditions between January and May 2023. Mildew grades were defined through manual analysis to construct a multimodal dataset for the rice mildew process. This study proposes a combined model (CNN-LSTM-A) that integrates convolutional neural networks (CNN), long short-term memory (LSTM) networks, and attention mechanisms to predict the mildew grade of stored rice. The proposed model was compared with LSTM, CNN-LSTM, and LSTM-Attention models. The results indicate that the proposed model outperforms the others, achieving a prediction accuracy of 98%. The model demonstrates superior accuracy and more stable performance. The generalization performance of the prediction model was evaluated using four experimental datasets with varying storage temperature and humidity conditions. The results show that the model achieves optimal prediction stability when the training set contains similar storage temperatures, with prediction accuracy exceeding 99.8%. This indicates that the model can effectively predict the mildew grades in rice under varying environmental conditions, demonstrating significant potential for grain mildew prediction and early warning systems.
霉变是粮食储存过程中损失的重要原因。霉菌的生长和代谢会导致谷仓内气体组成和温度发生变化。传感器技术和机器学习的最新进展使得在储存过程中预测谷物霉变成为可能。目前的研究主要集中在使用简单的机器学习方法预测霉变的发生或分级，而没有深入探讨霉变过程数据的时间序列特征。本研究设计并开发了一种监测装置，用于在模拟霉变过程实验中捕捉高质量的微环境参数和图像数据。使用来自浙江省的“甬优 15”水稻品种，在 2023 年 1 月至 5 月期间，在不同的温度和湿度条件下进行了五次模拟实验。通过手动分析定义霉变等级，构建了一个多模态数据集，用于研究水稻霉变过程。本研究提出了一种结合卷积神经网络（CNN）、长短时记忆（LSTM）网络和注意力机制的组合模型（CNN-LSTM-A），用于预测储存水稻的霉变等级。将所提出的模型与 LSTM、CNN-LSTM 和 LSTM-Attention 模型进行了比较。结果表明，所提出的模型表现优于其他模型，预测准确率达到 98%。该模型表现出更高的准确性和更稳定的性能。使用四个具有不同储存温度和湿度条件的实验数据集评估了预测模型的泛化性能。结果表明，当训练集包含相似的储存温度时，模型具有最佳的预测稳定性，预测准确率超过 99.8%。这表明该模型可以有效地预测不同环境条件下水稻的霉变等级，为粮食霉变预测和预警系统提供了重要的应用前景。
