Computer vision and machine learning enabled soybean root phenotyping pipeline.

ABSTRACT:

Background

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

Results

This high throughput phenotyping system, which has the

SUBMITTER: Falk KG 

PROVIDER: S-EPMC6977263 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature