AMSformer: A Transformer for Grain Storage Temperature Prediction Using Adaptive Multi-Scale Feature Fusion

Qinghui Zhang, Weixiang Zhang, Quanzhen Huang (), Chenxia Wan and Zhihui Li
Additional contact information
Qinghui Zhang: College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
Weixiang Zhang: College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
Quanzhen Huang: School of Electrical and Information Engineering, Henan University of Engineering, Zhengzhou 451191, China
Chenxia Wan: College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
Zhihui Li: College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China

Agriculture, 2024, vol. 15, issue 1, 1-17

Abstract: Grain storage temperature prediction is crucial for silo safety and can effectively prevent mold and mildew caused by increasing grain temperature and condensation due to decreasing grain temperature. However, current prediction methods lead to information redundancy when capturing temporal and spatial dependencies, which diminishes prediction accuracy. To tackle this issue, this paper introduces an adaptive multi-scale feature fusion transformer model (AMSformer). Firstly, the model utilizes the adaptive channel attention (ACA) mechanism to adjust the weights of different channels according to the input data characteristics and suppress irrelevant or redundant channels. Secondly, AMSformer employs the multi-scale attention mechanism (MSA) to more accurately capture dependencies at different time scales. Finally, the ACA and MSA layers are integrated by a hierarchical encoder (HED) to efficiently utilize adaptive multi-scale information, enhancing prediction accuracy. In this study, actual grain temperature data and six publicly available datasets are used for validation and performance comparison with nine existing models. The results demonstrate that AMSformer outperforms in 36 out of the 58 test cases, highlighting its significant advantages in prediction accuracy and efficiency.

Keywords: grain storage temperature prediction; information redundancy; adaptive channel attention mechanism; multi-scale attention mechanism; hierarchical encoder (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/1/58/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/1/58/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2024:i:1:p:58-:d:1555581

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().