Evidence map›Paper›PMID 41415161›Full record

ArticlePlant phenomics (Washington, D.C.)2025

RGB imaging and computer vision-based approaches for identifying spike number loci for wheat.

Lei Li, Muhammad Adeel Hassan, Duoxia Wang, Guoliang Wan, Sahila Beegum, Awais Rasheed, Xianchun Xia, Yong He, Yong Zhang, Zhonghu He 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

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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

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.

Lei LiState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Muhammad Adeel HassanAdaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD, 20705, USA.
Duoxia WangState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Guoliang WanState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Sahila BeegumAdaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD, 20705, USA.
Awais RasheedState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Xianchun XiaState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Yong HeInstitute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Yong ZhangState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Zhonghu HeState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Jindong LiuState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.
Yonggui XiaoState Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, National Wheat Improvement Centre, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100081, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The spike number (SN) is an important trait that significantly impacts grain yield in wheat. Manual counting of SN is time-consuming, hindering large-scale breeding efforts. Hence, there is an urgent need to develop efficient and accurate methodologies for SN counting. A YOLOX algorithm was used to determine the optimal growth stage for developing wheat spike detection models among recombinant inbred lines (RILs) across Zhongmai 175 ​× ​Lunxuan 987 and a diverse panel of 166 cultivars. We subsequently increased the precision of spike identification by developing a new YOLOX-P algorithm that incorporates the convolutional block attention module and increasing the resolution of the input images. We also used these SN data to identify underlying loci in the Zhongmai 578 ​× ​Jimai 22 RIL population. The results revealed that the late grain-filling stage presented the highest precision among the SN detection models, with accuracies ranging from 91.8 to 95.02 ​%. The improved YOLOX-P algorithm demonstrated higher mean average precision scores (5.30-5.99 ​%) and F1 scores (0.06) than did the YOLOX algorithm when it was applied to the same subsets. Three new SN loci, namely,

Indexed as

Common wheatImagingKASP markersQuantitative genetic analysisSpike number

Identifiers

PMID41415161
PMCPMC12710032

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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.