Evidence map›Paper›PMID 41669633›Full record

ArticleFrontiers in plant science2025

A deep learning architecture for leaf water potential prediction in

Xue-Wei Gong, Qing-Song Yu, Hong-Li Li, Zhuo-Qun Fang, Jia-Xu Guo, Zhao-Kui Li, Heng-Fang Wang, Zhong-Yi Pang, Yan-Hui Peng, Xue-Kai Sun and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

11 authors.

Xue-Wei GongCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China.
Qing-Song YuSchool of Computer Science, Shenyang Aerospace University, Shenyang, Liaoning, China.
Hong-Li LiSchool of Computer Science, Shenyang Aerospace University, Shenyang, Liaoning, China.
Zhuo-Qun FangSchool of Computer Science, Shenyang Aerospace University, Shenyang, Liaoning, China.
Jia-Xu GuoSchool of Computer Science, Shenyang Aerospace University, Shenyang, Liaoning, China.
Zhao-Kui LiSchool of Computer Science, Shenyang Aerospace University, Shenyang, Liaoning, China.
Heng-Fang WangKey Laboratory of Oasis Ecology of Ministry of Education, College of Ecology and Environment, Xinjiang University, Urumqi, Xinjiang, China.
Zhong-Yi PangState-owned Xinmin City Mechanical Forest Farm, Shenyang, Liaoning, China.
Yan-Hui PengState-owned Xinmin City Mechanical Forest Farm, Shenyang, Liaoning, China.
Xue-Kai SunCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China.
Guang-You HaoCAS Key Laboratory of Forest Ecology and Silviculture, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Leaf water potential (Ψ Methods: Here, we propose a deep learning framework (CIDL) that integrates: (1) a conditional generative adversarial network (CGAN) to generate balanced synthetic samples across the full Ψ Results: CIDL achieved a prediction accuracy of Discussion: These results demonstrate that the proposed CIDL framework provides a generalizable solution for small-sample physiological hyperspectral analysis and offers a reliable, non-destructive pathway for tree water-stress monitoring, with strong potential for applications in smart forestry management.

Indexed as

conditional generative adversarial networkdeep learningdistribution-aware regressionhyperspectral imagingleaf water potentialsmart forestry

Identifiers

PMID41669633
PMCPMC12883839

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.