Temperature data inside the granary is a crucial indicator for safe storage and quality preservation of grain. Currently, temperature measurement relies on temperature cables installed inside the grain piles. However, the sensors on the cables can only acquire temperature data at specific measurement points and cannot provide a complete overview of the entire grain pile. Most current temperature field generation methods based on conventional spatial interpolation techniques often suffer from significant errors. To achieve a more intuitive and accurate monitoring of temperature distribution in granary environments, this study proposes a high-resolution reconstruction model for grain temperature fields based on the 3DSRCNN_Resnet. The model firstly improves the SRCNN in 3D to obtain the 3DSRCNN model, so as to apply the SRCNN super-resolution reconstruction algorithm, which deals with 2D images, to the task of reconstructing the temperature field of a three-dimensional granary. Secondly, a nonlinear mapping module based on 3D residual blocks and a residual reconstruction module based on 3D deconvolution are proposed to adjust the network structure of the 3DSRCNN model. The experimental results show that compared with the traditional spatial interpolation method, the grain temperature data reconstructed by this model achieves better performance under MSE, MAE, PSNR and SSIM metrics. This model provides a novel approach for comprehensive and accurate understanding of temperature distribution within grain piles.

Yujie Cao, Jie Yuan, Junli Li, Bo Mao. High-Resolution Reconstruction Model for Spatial Temperature Distribution in Grain Storage Based on 3DSRCNN_Resnet. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 3: 73-80. https://doi.org/10.25236/AJCIS.2024.070311.

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