Journal Article

Computer vision and machine learning enabled soybean root phenotyping pipeline

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

2020

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Agricultural economics / Agricultural production / Analysis / Automation / barcoding / Biological Techniques / Biomedical and Life Sciences / Coders / Computer architecture / computer software / Computer vision / Corn / cost effectiveness / Crop resilience / Crops / Cultivars / Data analysis / Feature extraction / Genetic aspects / genetic improvement / Genetic variability / genetic variation / Genomics / Genotypes / Geographical distribution / growth and development / Image processing / Image segmentation / Laboratories / Learning algorithms / Life Sciences / Machine learning / Methodology / Morphology / Nondestructive testing / Object recognition / Optical character recognition / Phenomics / phenotype / Phenotypes / Phenotyping / Physiological aspects / Physiology / Plant breeding / Plant growth / Plant Sciences / Researchers / Robustness / Root / root growth / root systems / Roots (Botany) / RSA / Seedlings / Seeds / Software / Soybeans / Structure / Studies / Time series / time series analysis