Integrating Self-Attention Mechanisms and ResNet for Grain Storage Ventilation Decision Making: A Study

Abstract

Food security is a widely discussed topic globally. The key to ensuring the safety of food storage is to control temperature and humidity, with ventilation being an effective and fast method for temperature and humidity control. This paper proposes a new approach called “grain condition multimodal” based on the theory of computer multimodality. Under changing external environments, grain conditions can be classified according to different ventilation modes, including cooling ventilation, dehumidification ventilation, anti-condensation ventilation, heat dissipation ventilation, and quality adjustment ventilation. Studying intelligent ventilation decisions helps achieve grain temperature balance, prevent moisture condensation, control grain heating, reduce grain moisture, and create a low-temperature environment to improve grain storage performance. Combining deep learning models with data such as grain stack temperature and humidity can significantly improve the accuracy of ventilation decisions. This paper proposes a neural network model based on residual networks and self-attention mechanisms that performs better than basic models such as LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), GRU (Gated Recurrent Unit), and ResNet (Residual Network). The model’s accuracy, precision, recall, and F1 scores are 94.38%, 94.92%, 98.94%, and 96.89%, respectively.