Temperature Forecasting for Stored Grain: A Deep Spatiotemporal Attention Approach

Abstract

The development of Internet-of-Things (IoT) technology promotes the advances of grain condition detection and analysis systems. Temperature monitoring is a main element to maintain grain quality, and effective control of grain temperature is crucial to safe storage of grain. In this article, an encoder–decoder model with attention mechanism is proposed to accurately forecast the temperature of stored grain. Considering that the points on the gradient direction of the temperature surface have a great influence on the temperature of the target point, the Sobel operator is used to extract the local characteristics of the target point. In addition, considering the correlation structure in the sensory data, the attention mechanism is used to extract the global features of the target point. The extracted spatial features are fed into long short-term memory (LSTM) networks to obtain the long-term state information of spatial factors. LSTM unit and convolutional neural network are used to encode the spatial features of the target points. Taking meteorological factors as the external input of the decoder, temporal attention mechanism and LSTM unit are used to complete the decoding process and realize the prediction of grain temperature in the future. The results with real grain storage data show that the proposed model outperforms several schemes, including Kalman-modified the least absolute shrinkage and selection operator (Kalman-modified LASSO), temporal graph convolutional network (T-GCN), LSTM, CNN-LSTM, and convolutional LSTM (Conv-LSTM), with considerable gains.