Evidence map›Paper›PMID 42038795›Full record

ArticlePlant phenomics (Washington, D.C.)2026

Climate-robust evaluation of alfalfa seed maturity via an EMD-guided deep learning framework using multispectral imaging.

Zhicheng Jia, Fang Wang, Jiayi Fu, Ruohong Li, Juan Wang, Tianqi Zhu, Shiqiang Zhao, Chengzhi Lin, Liru Dou, Peisheng Mao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant phenomics (Washington, D.C.), 2026. 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

10 authors.

Zhicheng JiaCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Fang WangCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Jiayi FuCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Ruohong LiCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Juan WangCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Tianqi ZhuCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Shiqiang ZhaoCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Chengzhi LinCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Liru DouCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.
Peisheng MaoCollege of Grassland Science and Technology, China Agricultural University, Beijing, 100193, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Annual climatic and agronomic shifts induce phenotypic plasticity, causing standard deep learning models to fail in high-throughput automated phenotyping tasks, such as alfalfa (

Indexed as

Alfalfa seedDeep learningMultispectral imagingSeed maturityTransfer learning

Identifiers

PMID42038795
PMCPMC13109568

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.