Evidence map›Paper›PMID 42041000›Full record

ArticlePlant phenomics (Washington, D.C.)2025

Establishment of a high-throughput field defoliation data survey strategy combined with genome-wide association studies to reveal the genetic basis of defoliation in cotton.

Bowei Xu, Le Liu, Rumeng Zhao, Jiajie Yang, Bin Wu, Lili Lu, Xiantao Ai, Jingshan Tian, Fuguang Li, Kai Zheng and 2 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

12 authors.

Bowei XuState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Le LiuState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Rumeng ZhaoState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Jiajie YangState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Bin WuXinjiang Key Laboratory of Crop Gene Editing and Germplasm Innovation, Institute of Western Agricultural of CAAS, Changji, 831100, Xinjiang, China.
Lili LuState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Xiantao AiCollege of Smart Agriculture (Research Institute), Xinjiang University, Urumqi, 830046, Xinjiang, China.
Jingshan TianThe Key Laboratory of Oasis Eco-Agriculture, The Xinjiang Production and Construction Corps/College of Agronomy, Shihezi University, Shihezi, 832003, Xinjiang, China.
Fuguang LiState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Kai ZhengEngineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, 830052, China.
Liqiang FanState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.
Zuoren YangState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pre-harvest defoliation of cotton is a key agricultural measure to improve mechanical harvesting efficiency and raw cotton purity. Collecting data on cotton defoliation traits for genetic localization and thus breeding defoliation-prone varieties is an essential alternative to traditional defoliant spraying. Nevertheless, it is hampered by low throughput and artificial error in manual field surveys. In this study, a framework for collecting high-throughput defoliation data in large fields was established. Three spectral indices (MTCI, VDVI, CI) and leaf area index (LAI) were first screened as core predictors through hierarchical segmentation analysis in three levels: leaf number (LN), leaf number difference (LND), and defoliation rate (DR). Four deep learning architectures (CNN, BiGRU, CNN-BiGRU, and CNN-BiGRU-Attention) were developed, and the CNN-BiGRU-Attention hybrid model demonstrated superior performance at all three levels, with R

Indexed as

Deep learningDefoliationGenome-wide association studyLAIUAV

Identifiers

PMID42041000
PMCPMC13109323

What OpenQuestion holds

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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.