Campus Units

Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute, Agronomy

Document Type

Article

Publication Version

Published Version

Publication Date

7-28-2021

Journal or Book Title

Plant Phenomics

Volume

2021

First Page

9834746

DOI

10.34133/2021/9834746

Abstract

Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum) and are an important structure where atmospheric nitrogen (N2) is fixed into bioavailable ammonia (NH3) for plant growth and development. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is frequently done on a numerical scale that allows for rapid phenotyping, but is less informative and suffers from subjectivity. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations and has a good model fit (R2 = 0.99). SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location, and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.

Comments

This article is published as Jubery, Talukder Zaki, Clayton N. Carley, Arti Singh, Soumik Sarkar, Baskar Ganapathysubramanian, and Asheesh K. Singh. "Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP)." Plant Phenomics 2021 (2021): 9834746. DOI: 10.34133/2021/9834746. Posted with permission.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright Owner

Talukder Zaki Jubery et al.

Language

en

File Format

application/pdf

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