CADFFNet: a dual-branch neural network for non-destructive detection of cigar leaf moisture content during air-curing stage
Generally, the proposed CADFFNet offers an efficient and convenient method for non-destructive detection of CLMC, providing a theoretical basis for automating the air-curing process. It also provides a new perspective for moisture content prediction during the drying process of other crops, such as tea, asparagus, and mushrooms.
Authors
- Yihao Pan
- Bingyang Liu (ORCID: https://orcid.org/0000-0002-4572-4956)
- Kai Zhang (ORCID: https://orcid.org/0000-0002-3615-5616)
- Zhen-Hua Wang
- Xiangdong Shi (ORCID: https://orcid.org/0009-0008-3616-701X)
- Songshuang Ding (ORCID: https://orcid.org/0000-0002-2192-3993)
- Z Xing (ORCID: https://orcid.org/0009-0004-9584-5811)
- Yaqi Shi
Institutions
- Hefei University of Technology (CN)
- China Tobacco (CN)
- Tobacco Research Institute (CN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-11-05
- DOI
- https://doi.org/10.3389/fpls.2025.1698427
- Primary Topic
- Food Drying and Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00