Evidence map›Paper›PMID 42668463›Full record

ArticlePlant phenomics (Washington, D.C.)2026

Assessing cotton boll-opening concentration for harvest decision-making via foundation model-enhanced cross-scale phenotyping.

Mian Chen, Daowu Hu, Cheng Peng, Jiajin Zhang, Haochong Chen, Xiongming Du, Shoupu He, Rui Zhang, Xiaoli Geng, Shunfu Xiao and 3 more

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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.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

13 authors.

Mian ChenChina Agricultural University, College of Land Science and Technology, Beijing, 100193, China.
Daowu HuState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
Cheng PengChina Agricultural University, College of Land Science and Technology, Beijing, 100193, China.
Jiajin ZhangChina Agricultural University, College of Land Science and Technology, Beijing, 100193, China.
Haochong ChenChina Agricultural University, College of Land Science and Technology, Beijing, 100193, China.
Xiongming DuState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
Shoupu HeState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
Rui ZhangBiotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Xiaoli GengState Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang, 455000, China.
Shunfu XiaoChina Agricultural University, College of Land Science and Technology, Beijing, 100193, China.
Yan GuoChina Agricultural University, College of Land Science and Technology, Beijing, 100193, China.
Xiaoli TianEngineering Research Center of Plant Growth Regulator, Ministry of Education, State Key Laboratory of Plant Environmental Resilience, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100193, China.
Yuntao MaChina Agricultural University, College of Land Science and Technology, Beijing, 100193, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Boll-opening concentration is critical for mechanical cotton harvesting, yet it is still assessed mainly by manual records and single time-point indicators that miss temporal dynamics. To bridge the lack of a unified workflow linking vision foundation models, multi-temporal boll-opening monitoring, and harvest decision-making, we developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX. DINO-BollGX couples a DINO v3 backbone, a RetinaNet detection head, and an adaptive refinement-and-suppression module for robust open-boll detection under complex field conditions. Using multi-temporal UAV imagery collected over two years for 383 cultivars, we reconstructed plot-scale time series of open-boll counts, derived dynamic features describing progression and intensity changes, and proposed a Cotton Boll-Opening Temporal Stability Index (CTSI) to quantify boll-opening rhythm and concentration; CTSI was further integrated with a time-based risk function to generate harvest decision curves. Under unified data and training settings, DINO-BollGX achieved precision = 0.91, F1 = 0.88, and AP@0.50 = 0.80, and provided accurate boll-count estimation (R

Indexed as

Cotton boll-openingCotton boll-opening temporal stability index (CTSI)Harvest-timing optimizationUAV high-throughput phenotypingVision foundation model

Identifiers

PMID42668463
PMCPMC13524563

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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.