ArticlePlant phenomics (Washington, D.C.)2023
Application of an Improved 2-Dimensional High-Throughput Soybean Root Phenotyping Platform to Identify Novel Genetic Variants Regulating Root Architecture Traits.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Breeding Climate-Resilient Soybeans for 2050 and Beyond: Leveraging Novel Technologies to Mitigate Yield Stagnation and Climate Change Impacts.Plants (Basel, Switzerland) · 2026Review
- RGB imaging-based evaluation of waterlogging tolerance in cultivated and wild chrysanthemums.Plant phenomics (Washington, D.C.) · 2025Article
- Integration of Genetic and Imaging Data to Detect QTL for Root Traits in Interspecific Soybean Populations.International journal of molecular sciences · 2025Article
- Soybean genomics research community strategic plan: A vision for 2024-2028.The plant genome · 2024Article
- The Invisible Frontline: High-Tech Root Imaging for Crop Stress Adaptation.Physiologia plantarumReview
Corrections and comments
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Authors and funding
15 authors.
Funding
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Abstract
Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (
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Registered trials
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