An Interpretable Prediction Method for Tobacco Drying Process Based on CGTNN

An Interpretable Prediction Method for Tobacco Drying Process Based on CGTNN

The moisture content of tobacco is a critical quality indicator in the tobacco processing, it is essential to control the moisture content of the tobacco within an appropriate range through the drying process. However, production often faces large delays in moisture measurement, leading to the diffi...

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Bibliographic Details
Main Authors: Wencai Wang, Chen Yang, Wenwei Niu, Sidi Lin, Qiang Gao, Zhe Cao, Jianning Chen, Jianzhong Li, Zhengkui Li
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Tobacco drying
graph neural networks
correlation analysis
spatial and temporal analysis
deep learning interpretability
Online Access:https://ieeexplore.ieee.org/document/10843206/
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