Evidence map›Paper›PMID 42041020›Full record

ArticlePlant phenomics (Washington, D.C.)2025

UAV-LiDAR high-throughput time-series phenotyping and genome-wide association analysis reveal the genetic basis of plant height in peanut (

Jiangtao Tan, Shiyuan Liu, Guowei Li, Weiguang Yang, Minmin Liang, Xi Li, Yifei Chen, Huanxin Zou, Baixiang Wang, Zhi Pan and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plant phenomics (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Jiangtao TanGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Shiyuan LiuGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Guowei LiShandong Academy of Agricultural Sciences, Jinan, Shandong, 250100, China.
Weiguang YangCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, 510642, China.
Minmin LiangGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Xi LiGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Yifei ChenGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Huanxin ZouGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Baixiang WangGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Zhi PanGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Jianguo WangShandong Academy of Agricultural Sciences, Jinan, Shandong, 250100, China.
Yubin LanCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou, 510642, China.
Tingting ChenGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.
Lei ZhangGuangdong Provincial Key Laboratory of Plant Molecular Breeding, College of Agriculture, South China Agricultural University, Guangzhou, 510642, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Plant height (PH) is closely linked to yield potential, lodging resistance, and mechanized harvesting efficiency in peanut cultivation. However, breeding efforts for optimized PH are hindered by limited understanding of its genetic architecture. In this study, we utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials. Using UAV-LiDAR data, we precisely measured time-series PH and applied Gaussian fitting and principal component analysis (PCA) to extract five dynamic growth parameters: parameter

Indexed as

Functional researchGenomic analysisPeanutPlant heightUAV-LiDAR

Identifiers

PMID42041020
PMCPMC13109324

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.