# Research on Storage Grain Temperature Prediction Method Based on FTA-CNN-SE-LSTM with Dual-Domain Data Augmentation and Deep Learning

## TL;DR

This paper introduces a new method for predicting grain storage temperatures using advanced data augmentation and a deep learning model to improve accuracy and address data limitations.

## Contribution

A novel dual-domain data augmentation method and an enhanced LSTM-based model with CNN and SE modules for improved temperature prediction in grain storage.

## Key findings

- The proposed FTA-CNN-SE-LSTM model reduces MAE and RMSE by 74.77% and 74.02%, respectively, compared to the original LSTM.
- The model effectively handles small sample sizes in grain storage data through time and frequency domain augmentation.
- The method improves prediction accuracy and helps prevent issues caused by abnormal grain pile temperatures.

## Abstract

Temperature plays a crucial role in the grain storage process and food security. Due to limitations in grain storage data acquisition in real-world scenarios, this paper proposes a data augmentation method for grain storage data that operates in both the time and frequency domains, as well as an enhanced grain storage temperature prediction model. To address the issue of small sample sizes in grain storage temperature data, Gaussian noise is added to the grain storage temperature data in the time domain to highlight the subtle variations in the original data. The fast Fourier transform (FFT) is employed in the frequency domain to highlight periodicity and trends in the grain storage temperature data. The prediction model uses a long short-term memory (LSTM) network, enhanced with convolution layers for feature extraction and a Squeeze-and-Excitation Networks (SENet) module to suppress unimportant features and highlight important ones. Experimental results show that the FTA-CNN-SE-LSTM compares with the original LSTM network, and the MAE and RMSE are reduced by 74.77% and 74.02%, respectively. It solves the problem of data limitation in the actual grain storage process, greatly improves the accuracy of grain storage temperature prediction, and can accurately prevent problems caused by abnormal grain pile temperature.