Recent technological advances in high-throughput phenotyping have created new opportunities for the prediction of complex traits. In particular, phenomic prediction using hyperspectral reflectance could capture various signals that affect phenotypes genomic prediction might not explain. A total of 360 inbred maize (Zea mays L.) lines with or without plant growth-promoting bacterial inoculation management under nitrogen stress were evaluated using 150 spectral wavelengths ranging from 386 to 1,021 nm and 13,826 single-nucleotide polymorphisms. Six prediction models were explored to assess the predictive ability of hyperspectral and genomic data for inoculation status and plant growth-related traits. The best models for hyperspectral prediction were partial least squares and automated machine learning. The Bayesian ridge regression and BayesB were the best performers for genomic prediction. Overall, hyperspectral prediction showed greater predictive ability for shoot dry mass and stalk diameter, whereas genomic prediction was better for plant height. The prediction models that simultaneously accommodated both hyperspectral and genomic data resulted in a predictive ability as high as that of phenomics or genomics alone. Our results highlight the usefulness of hyperspectral-based phenotyping for management and phenomic prediction studies.

中文翻译：

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利用高光谱图像和基因组数据对热带玉米植物促生菌接种状态的分类和生长相关性状的预测

最近在高通量表型分析方面的技术进步为复杂性状的预测创造了新的机会。特别是，使用高光谱反射率的表型预测可以捕获影响表型基因组预测可能无法解释的各种信号。自交系玉米共计 360 份（Zea maysL.) 使用 386 至 1,021 nm 的 150 个光谱波长和 13,826 个单核苷酸多态性评估在氮胁迫下有或没有植物促生长细菌接种管理的品系。探索了六种预测模型，以评估高光谱和基因组数据对接种状态和植物生长相关性状的预测能力。高光谱预测的最佳模型是偏最小二乘法和自动机器学习。贝叶斯岭回归和 BayesB 是基因组预测的最佳表现者。总体而言，高光谱预测显示出对茎干质量和茎直径的更强预测能力，而基因组预测对植物高度的预测能力更好。同时容纳高光谱和基因组数据的预测模型导致预测能力与单独的表型组学或基因组学一样高。我们的结果强调了基于高光谱的表型分析对于管理和表型预测研究的有用性。