Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Because of the tremendous increase in collected data in breeding programs and the slow rate of genetic gain increase, it is required to explore the potential of artificial intelligence in analyzing the data. The main objectives of this study include optimization of multitrait (MT) machine- and deep-learning models for predicting grain yield and grain protein content in wheat (Triticum aestivum L.) using spectral information. This study compares the performance of four machine- and deep-learning-based unitrait (UT) and MT models with traditional genomic best linear unbiased predictor (GBLUP) and Bayesian models. The dataset consisted of 650 recombinant inbred lines (RILs) from a spring wheat breeding program grown for three years (2014–2016), and spectral data were collected at heading and grain filling stages. The MT-GS models performed 0–28.5 and −0.04 to 15% superior to the UT-GS models. Random forest and multilayer perceptron were the best performing machine- and deep-learning models to predict both traits. Four explored Bayesian models gave similar accuracies, which were less than machine- and deep-learning-based models and required increased computational time. Green normalized difference vegetation index (GNDVI) best predicted grain protein content in seven out of the nine MT-GS models. Overall, this study concluded that machine- and deep-learning-based MT-GS models increased prediction accuracy and should be employed in large-scale breeding programs.

中文翻译：

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在小麦育种计划中使用光谱信息进行基因组选择的多性状机器和深度学习模型

育种价值的预测是植物育种的核心，并通过采用基因组选择 (GS) 发生了革命性的变化。使用应用于植物复杂性状的机器和深度学习算法可以提高预测准确性。由于育种计划中收集的数据的巨大增加和遗传增益增加的缓慢速度，需要挖掘人工智能在数据分析中的潜力。本研究的主要目标包括优化多性状 (MT) 机器和深度学习模型，以预测小麦 ( Triticum aestivum ) 的籽粒产量和籽粒蛋白质含量。L.) 使用光谱信息。本研究比较了四种基于机器和深度学习的单特征 (UT) 和 MT 模型与传统基因组最佳线性无偏预测器 (GBLUP) 和贝叶斯模型的性能。该数据集由 650 个重组自交系 (RIL) 组成，来自一个种植了三年（2014-2016 年）的春小麦育种计划，并在抽穗和灌浆阶段收集了光谱数据。MT-GS 模型的性能比 UT-GS 模型高 0–28.5 和 -0.04 到 15%。随机森林和多层感知器是预测这两个特征的最佳机器和深度学习模型。四个探索过的贝叶斯模型给出了类似的准确度，低于基于机器和深度学习的模型，并且需要增加计算时间。绿色归一化植被指数 (GNDVI) 在 9 个 MT-GS 模型中的 7 个中最好地预测了谷物蛋白质含量。总体而言，这项研究得出的结论是，基于机器和深度学习的 MT-GS 模型提高了预测准确性，应该用于大规模育种计划。