A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturi
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Plant Methods Open Access
RESEARCH
A simple, cost‑effective high‑throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat Morteza Shabannejad1 , Mohammad‑Reza Bihamta2* , Eslam Majidi‑Hervan1 , Hadi Alipour3 and Asa Ebrahimi1
Abstract Background: High-throughput phenotyping and genomic selection accelerate genetic gain in breeding programs by advances in phenotyping and genotyping methods. This study developed a simple, cost-effective high-through‑ put image analysis pipeline to quantify digital images taken in a panel of 286 Iran bread wheat accessions under terminal drought stress and well-watered conditions. The color proportion of green to yellow (tolerance ratio) and the color proportion of yellow to green (stress ratio) was assessed for each canopy using the pipeline. The estimated tolerance and stress ratios were used as covariates in the genomic prediction models to evaluate the effect of change in canopy color on the improvement of the genomic prediction accuracy of different agronomic traits in wheat. Results: The reliability of the high-throughput image analysis pipeline was proved by three to four times of improve‑ ment in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covari‑ ates in the univariate genomic selection models. The higher prediction accuracies were attained for days to maturity when both tolerance and stress ratios were used as fixed effects in the univariate models. The results of this study indi‑ cated that the Bayesian ridge regression and ridge regression-best linear unbiased prediction methods were superior to other genomic prediction methods which were used in this study under terminal drought stress and well-watered conditions, respectively. Conclusions: This study provided a robust, quick, and cost-effective machine learning-enabled image-phenotyping pipeline to improve the genomic prediction accuracy for days to maturity in wheat. The results encouraged the inte‑ gration of phenomics and genomics in breeding programs. Keywords: High-throughput phenotyping, Image analysis, Pipeline, Genomic prediction, Days to maturity, Wheat
*Correspondence: [email protected] 2 Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences and Engineering, College of Agriculture and Natural Resources, University of Tehran, P.O. Box 4111, Karaj, Alborz, Iran Full list of author information is available at the end of the article
Background The efficient and precise phenotyping of a large population is one of the main tasks in breeding programs [1]. For example, the recording process of grain yield is currently difficult, time-consuming, and costly. The visual assessments are normally incapable of attaining small but important phenotypic variations [2]. Even with good scoring, only small fractions of phenotypes
© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution a
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