Parmley Kyle, Nagasubramanian Koushik, Sarkar Soumik, Ganapathysubramanian Baskar, Singh Asheesh K
Department of Agronomy, Iowa State University, Ames, IA, USA.
Department of Electrical Engineering, Iowa State University, Ames, IA, USA.
Plant Phenomics. 2019 Jul 28;2019:5809404. doi: 10.34133/2019/5809404. eCollection 2019.
The rate of advancement made in phenomic-assisted breeding methodologies has lagged those of genomic-assisted techniques, which is now a critical component of mainstream cultivar development pipelines. However, advancements made in phenotyping technologies have empowered plant scientists with affordable high-dimensional datasets to optimize the operational efficiencies of breeding programs. Phenomic and seed yield data was collected across six environments for a panel of 292 soybean accessions with varying genetic improvements. Random forest, a machine learning (ML) algorithm, was used to map complex relationships between phenomic traits and seed yield and prediction performance assessed using two cross-validation (CV) scenarios consistent with breeding challenges. To develop a prescriptive sensor package for future high-throughput phenotyping deployment to meet breeding objectives, feature importance in tandem with a genetic algorithm (GA) technique allowed selection of a subset of phenotypic traits, specifically optimal wavebands. The results illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping (e.g., seed yield harvest). While we illustrate with soybean, this study establishes a template for deploying multitrait phenomic prediction that is easily amendable to any crop species and any breeding objective.
表型组辅助育种方法的发展速度落后于基因组辅助技术，而基因组辅助技术如今已是主流品种培育流程的关键组成部分。然而，表型分析技术的进步为植物科学家提供了经济实惠的高维数据集，以优化育种计划的运作效率。针对一组292份具有不同遗传改良程度的大豆种质，在六种环境下收集了表型组和种子产量数据。随机森林作为一种机器学习（ML）算法，被用于绘制表型组性状与种子产量之间的复杂关系，并使用与育种挑战一致的两种交叉验证（CV）方案评估预测性能。为了开发一个用于未来高通量表型分析部署以实现育种目标的规范性传感器包，结合遗传算法（GA）技术的特征重要性使得能够选择一组表型性状子集，特别是最优波段。研究结果揭示了融合机器学习和优化技术以识别一系列季中表型组性状的能力，这将使育种计划减少对资源密集型季末表型分析（如种子产量收获）的依赖。虽然我们以大豆为例进行说明，但本研究建立了一个部署多性状表型组预测的模板，该模板可轻松适用于任何作物品种和任何育种目标。
