Evidence map›Paper›PMID 42038783›Full record

ArticlePlant phenomics (Washington, D.C.)2026

Combining RGB imaging with a two-stage deep learning method to reveal genetic variation of wheat sprouting traits.

Bingxi Qin, Xiaojing Zhu, Hui Deng, Xin Liu, Qian Wu, Honghao Zhou, Shuming Fan, Haijiang Min, Shaowei Liang, Wei Sang and 11 more

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

21 authors.

Bingxi QinAcademy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
Xiaojing ZhuState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Hui DengState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Xin LiuState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Qian WuState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Honghao ZhouAcademy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
Shuming FanNational Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Ecophysiology, Collaborative Innovation Centre for Modern Crop Production, Ministry of Agriculture and Rural Affairs/College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
Haijiang MinNational Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Ecophysiology, Collaborative Innovation Centre for Modern Crop Production, Ministry of Agriculture and Rural Affairs/College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
Shaowei LiangNational Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Ecophysiology, Collaborative Innovation Centre for Modern Crop Production, Ministry of Agriculture and Rural Affairs/College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
Wei SangInstitute of Crop Science, Xinjiang Academy of Agri-Reclamation Sciences, Shihezi, 832000, Xinjiang, China.
Mei HuangNational Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Ecophysiology, Collaborative Innovation Centre for Modern Crop Production, Ministry of Agriculture and Rural Affairs/College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
Jiawei ChenAcademy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
Chenzhao GaoState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Junhao TuState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Shijie ZhaiState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Xueli HuState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Mengshi LiuState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Yun ZhouState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Dong JiangAcademy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
Zhen ZhangState Key Laboratory of Crop Stress Adaptation and Improvement, the Zhongzhou Laboratory for Integrative Biology, College of Agriculture, School of Life Sciences, Henan University, Jinming Avenue 1, Kaifeng, 475004, China.
Qing LiAcademy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Wheat emergence rate and emergence uniformity are key indicators for evaluating seed vigor and sowing quality, and they play an important role in wheat growth and yield formation. Traditional methods for measuring emergence rate and evaluating emergence uniformity rely on manual assessment, which is inefficient, highly subjective, and unable to meet the demand for large scale, high efficiency, and precise acquisition of wheat emergence data. In this study, RGB images and a two-stage deep learning algorithm were used to extract and analyze seedling traits of 420 wheat varieties under two nitrogen levels, and the results were applied to genome wide association studies to elucidate the genetic basis. The two-stage algorithm integrates a Bidirectional Feature Pyramid Network, small object detection layer, large size image input, and FasterNet to improve detection and instance segmentation speed and accuracy. The proposed method achieved an emergence rate accuracy of 0.929, with R

Indexed as

Deep learningEmergence rateEmergence uniformityGenome-wide association studyWheat

Identifiers

PMID42038783
PMCPMC13109579

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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.