Computer vision and machine learning enabled soybean root phenotyping pipeline

Kevin G Falk; Talukder Z Jubery; Seyed V Mirnezami; Kyle A Parmley; Soumik Sarkar; Arti Singh; Baskar Ganapathysubramanian; Asheesh K Singh

doi:10.1186/s13007-019-0550-5

Computer vision and machine learning enabled soybean root phenotyping pipeline

Plant Methods. 2020 Jan 23:16:5. doi: 10.1186/s13007-019-0550-5. eCollection 2020.

Authors

Kevin G Falk¹, Talukder Z Jubery², Seyed V Mirnezami², Kyle A Parmley¹, Soumik Sarkar², Arti Singh¹, Baskar Ganapathysubramanian², Asheesh K Singh¹

Affiliations

¹ 1Department of Agronomy, Iowa State University, Ames, USA.
² 2Department of Mechanical Engineering, Iowa State University, Ames, USA.

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 capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

Conclusions: This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Keywords: Breeding; Computer vision; Image analysis; Machine learning; Phenomics; Phenotyping; RSA; Root; Soybean; Time series.