Evidence map›Paper›PMID 42724035›Full record

ArticleFrontiers in plant science2026

Rice aboveground biomass estimation based on three-dimensional dry matter distribution integration model.

Dandan Liu, Junjie Li, Min He, Yihan Wang, Jiahao Chen, Zhaoyang Pan, Teng Long, Yubin Lan, Zhenjiang Xu, Hong Liu and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 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

11 authors.

Dandan LiuCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Junjie LiCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Min HeCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Yihan WangCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Jiahao ChenCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Zhaoyang PanCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Teng LongCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Yubin LanCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.
Zhenjiang XuCollege of Agriculture, South China Agricultural University, Guangzhou, China.
Hong LiuCollege of Agriculture, South China Agricultural University, Guangzhou, China.
Yongbing LongCollege of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate quantification of rice aboveground biomass (AGB) is critical for crop monitoring but remains challenging due to the complex nonlinearity arising from the coupling of plant density, spatial structure, and internal dry matter distribution. To address the limitations of single-source remote sensing, this study proposed a Three-Dimensional Dry Matter Distribution Integration (3D-DMI) model, which establishes a physically interpretable framework decomposing AGB into dry matter density (

Indexed as

3D-DMI frameworkdual-step feature selectionmachine learningmulti-source UAV remote sensingrice aboveground biomass (AGB)

Identifiers

PMID42724035
PMCPMC13558186

What OpenQuestion holds

Textmetadata
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.