Evidence map›Paper›PMID 41977730›Full record

ArticlePlants (Basel, Switzerland)2026

Integrating Hyperspectral Data and Deep Learning for Non-Destructive Prediction of Tea Quality Parameters Across Different Physical States of Tea Leaves and Growth Periods.

Guanzi Zhou, Haotian Ji, Rongyu Pan, Xiaowei Yang, Suhui Zhao, Lei Yang, Xiaohan Shang, Huijie Zhang, Hanchi Zhang, Xiaojun Liu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plants (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Guanzi ZhouCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Haotian JiCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Rongyu PanCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Xiaowei YangGuizhou Tea Research Institute, Guizhou Academy of Agricultural Sciences, Guiyang 550006, China.
Suhui ZhaoCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Lei YangCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Xiaohan ShangCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Huijie ZhangCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Hanchi ZhangCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Xiaojun LiuNational Engineering and Technology Center for Information Agriculture, MOE Engineering Research Center of Smart Agricultural, MARA Key Laboratory for Crop System Analysis and Decision Making, Jiangsu Key Laboratory for Information Agriculture, Institute of Smart Agriculture, Nanjing Agricultural University, Nanjing 210095, China.ORCID 0000-0001-7593-085X
Yuanchun MaCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.
Xujun ZhuCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.ORCID 0000-0001-8932-4324
Jie JiangCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.ORCID 0009-0007-2516-8247
Wanping FangCollege of Horticulture, Nanjing Agricultural University, Nanjing 210095, China.

Funding

Changzhou Agricultural Science and Technology Innovation Project 202506China Agriculture Research System of MOF and MARA CARS-19General Program of China Postdoctoral Science Foundation 2024M761440Jiangsu Province Industry University Research Cooperation Project BY20240370Postdoctoral Fellowship Program of CPSF GZC20240720Science and Technology Support Project of Guizhou Province ZK. [2021]154Suzhou Science and Technology Project SNG2023001Wellcome Trust 202510
6 · The paper itself

Abstract

Achieving rapid and non-destructive assessment of tea quality is essential for intelligent tea production and quality control. In this study, an integrated hyperspectral and deep learning framework was developed to estimate tea quality constituents across seasons and physical states. Samples included field fresh leaves, dried tea leaves, and tea powder, were collected in spring, summer, and autumn. Tea polyphenols and catechins were predicted using original reflectance, harmonic features, and wavelet features fused into multi-domain indices. Extreme gradient boosting, Gaussian process regression, and convolutional neural networks (CNN) were systematically compared to construct the quality estimation models. The result showed that three-feature indices consistently outperformed two-feature indices, yielding R

Indexed as

deep learninghyperspectral datamulti-domain spectral featuresnon-destructive estimationtea quality

Identifiers

PMID41977730
PMCPMC13074506

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.