A simple, cost-effective high-throughput image analysis pipeline improves genomic prediction accuracy for days to maturity in wheat

Plant Methods

Mohammad-Reza Bihamta¹  Morteza Shabannejad²  Eslam Majidi-Hervan²  Asa Ebrahimi²  Hadi Alipour³ 

[1] 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;Department of Plant Breeding and Biotechnology, Faculty of Agricultural Sciences and Food Industries, Science and Research Branch, Islamic Azad University, P.O. Box 14515/775, Tehran, Iran;Department of Plant Production and Genetics, Faculty of Agriculture and Natural Resources, Urmia University, P.O. Box 165, Urmia, Iran;

关键词: High-throughput phenotyping;    Image analysis;    Pipeline;    Genomic prediction;    Days to maturity;    Wheat;   

DOI  :  10.1186/s13007-020-00686-2

来源: Springer

【 摘 要 】

BackgroundHigh-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-throughput 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.ResultsThe reliability of the high-throughput image analysis pipeline was proved by three to four times of improvement in the accuracy of genomic predictions for days to maturity with the use of tolerance and stress ratios as covariates 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 indicated 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.ConclusionsThis 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 integration of phenomics and genomics in breeding programs.

【 授权许可】

CC BY