ArticleFrontiers in plant science2020
Image-Based Machine Learning Characterizes Root Nodule in Soybean Exposed to Silicon.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 38 citations in OpenAlex.
- Host-specific fluorescence dynamics in legume-rhizobium symbiosis during nodulation.Applied and environmental microbiology · 2026Article
- YOLO-based high-throughput phenotyping pipeline for soybean nodulation traits in genomic research.Frontiers in plant science · 2026Article
- Swin-Unet++: a study on phenotypic parameter analysis of cabbage seedling roots.Plant methods · 2025Article
- Comparative transcriptomic analysis of the nodulation-competent zone and inference of transcription regulatory network in silicon applied Glycine max [L.]-Merr. Roots.Plant cell reports · 2024Article
- Automatic Evaluation of Soybean Seed Traits Using RGB Image Data and a Python Algorithm.Plants (Basel, Switzerland) · 2023Article
- Spatial and Texture Analysis of Root System distribution with Earth mover's Distance (STARSEED).Plant methods · 2023Article
- High-Throughput Phenotypic Characterization and Diversity Analysis of Soybean Roots (Plants (Basel, Switzerland) · 2022Article
- High-Throughput Root Imaging Analysis Reveals Wide Variation in Root Morphology of Wild Adzuki bean (Plants (Basel, Switzerland) · 2022Article
- Silicon Effects on the Root System of Diverse Crop Species Using Root Phenotyping Technology.Plants (Basel, Switzerland) · 2021Review
- Competition, Nodule Occupancy, and Persistence of Inoculant Strains: Key Factors in theFrontiers in plant science · 2021Review
- Article
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Authors and funding
6 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Silicon promotes nodule formation in legume roots which is crucial for nitrogen fixation. However, it is very time-consuming and laborious to count the number of nodules and to measure nodule size manually, which led nodule characterization not to be study as much as other agronomical characters. Thus, the current study incorporated various techniques including machine learning to determine the number and size of root nodules and identify various root phenotypes from root images that may be associated with nodule formation with and without silicon treatment. Among those techniques, the machine learning for characterizing nodule is the first attempt, which enabled us to find high correlations among root phenotypes including root length, number of forks, and average link angles, and nodule characters such as number of nodules and nodule size with silicon treatments. The methods here could greatly accelerate further investigation such as delineating the optimal concentration of silicon for nodule formation.
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Registered trials
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