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Related Experiment Video

Updated: Oct 24, 2025

Polysome Purification from Soybean Symbiotic Nodules

Polysome Purification from Soybean Symbiotic Nodules

Published on: July 1, 2022

Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP).

Talukder Zaki Jubery¹, Clayton N Carley², Arti Singh²

¹Department of Mechanical Engineering, Iowa State University, Ames, IA, USA.

Plant Phenomics (Washington, D.C.)

|August 16, 2021

Summary

Soybean Nodule Acquisition Pipeline (SNAP) automates nodule counting on soybean roots using deep learning. This improves nitrogen fixation research and breeding for better crop yields.

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Last Updated: Oct 24, 2025

Polysome Purification from Soybean Symbiotic Nodules

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Published on: July 1, 2022

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Plant Promoter Analysis: Identification and Characterization of Root Nodule Specific Promoter in the Common Bean

Plant Promoter Analysis: Identification and Characterization of Root Nodule Specific Promoter in the Common Bean

Published on: December 23, 2017

Area of Science:

Agricultural Science
Plant Biology
Biotechnology

Background:

Soybean root nodules, formed by Bradyrhizobium japonicum symbiosis, are crucial for fixing atmospheric nitrogen into ammonia for plant growth.
Current nodule quantification methods are labor-intensive, subjective, and lack detailed information, hindering efficient phenotyping and breeding.

Purpose of the Study:

To develop and validate an automated Soybean Nodule Acquisition Pipeline (SNAP) for accurate and high-throughput nodule quantification on soybean roots.
To overcome the limitations of manual counting and subjective assessments in nodule analysis.

Main Methods:

Developed SNAP by integrating RetinaNet and UNet deep learning models for nodule detection and segmentation.
Trained and validated SNAP using a diverse dataset of 691 soybean roots across various genotypes, growth stages, and locations.
Achieved a high model fit with R² = 0.99.

Main Results:

SNAP significantly reduces labor and inconsistencies associated with manual nodule counting.
The pipeline provides quantifiable data on nodule number, growth, location, and distribution.
High throughput phenotyping enables early-stage assessment of genetic and environmental factors influencing nodulation.

Conclusions:

SNAP offers a robust and efficient solution for soybean nodule quantification, enhancing research capabilities.
The technology facilitates the assessment of factors affecting nodulation, potentially leading to improved nitrogen use efficiency in soybean and other legumes.
SNAP advances the understanding of plant-Bradyrhizobium interactions and supports breeding programs for enhanced crop performance.