Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods
values of 0.81 and 0.76 for tea polyphenols and catechins, respectively. This framework provides an accurate, non-destructive tool for tea quality evaluation and traceability, offering technical support for intelligent agriculture and quality control across the tea industry chain.
Authors
- WANPING FANG
- Lili Yang
- Hanchi Zhang
- Huijie Zhang (ORCID: https://orcid.org/0000-0001-8006-4845)
- Haotian Ji
- Suhui Zhao
- Rongyu Pan
- Yuanchun Ma
- Xiaohan Shang
- Guanzi Zhou
- Xujun Zhu
Institutions
- Nanjing Agricultural University (CN)
- Guizhou Academy of Agricultural Sciences (CN)
- Tea Research Institute (CN)
- Yunnan Open University (CN)
Publication Details
- Journal
- Plants
- Published
- 2026-03-31
- DOI
- https://doi.org/10.3390/plants15071071
- Primary Topic
- Spectroscopy and Chemometric Analyses
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Postdoctoral Science Foundation
- Government of Jiangsu Province
- Science and Technology Program of Suzhou