Genetic Dissection of Image-Derived Pod-Related Traits in an Interspecific Soybean RIL Population.

Fangguo Chang, Tuanjie Zhao, Shunchang Su, Xiaohan Ruan, Liping Wei

Abstract read

In one paragraph

Article in Plants (Basel, Switzerland), 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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5 · Who and what money

Authors and funding

5 authors.

Fangguo ChangCollege of Agronomy, Gansu Agricultural University, Lanzhou 730070, China.

Tuanjie ZhaoKey Laboratory of Biology and Genetics Improvement of Soybean, Ministry of Agriculture, Zhongshan Biological Breeding Laboratory (ZSBBL), National Innovation Platform for Soybean Breeding and Industry-Education Integration, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, College of Agriculture, Nanjing Agricultural University, Nanjing 211800, China.ORCID 0000-0002-8528-0405

Shunchang SuCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.

Xiaohan RuanCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.

Liping WeiCollege of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China.

Funding

Gansu Agricultural University GAU-KYQD-2022-06

6 · The paper itself

Abstract

Soybean pod-related traits are important agronomic characteristics associated with seed development, domestication, cultivar identification, and breeding improvement. However, conventional phenotyping methods mainly rely on manual measurements, which are time-consuming and labor-intensive and capture only limited dimensions of pod variation, while the genetic basis of skeleton- and curvature-based pod descriptors remains insufficiently characterized in biparental populations. In this study, eight quantitative traits representing pod size, shape, and color components were extracted from an existing mature pod image dataset of an interspecific soybean recombinant inbred line (RIL) population using the established deep learning-based image phenotyping framework. These traits exhibited substantial phenotypic variation, with across-year entry-mean broad-sense heritability

Indexed as

deep learninglinkage mappingphenotypingQTLsoybean

Identifiers

PMID42796801

DOI10.3390/plants15182769

PMCPMC13609990

What OpenQuestion holds

Textmetadata

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.