van Dijk Aalt Dirk Jan, Kootstra Gert, Kruijer Willem, de Ridder Dick
Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands.
Biometris, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands.
iScience. 2020 Dec 5;24(1):101890. doi: 10.1016/j.isci.2020.101890. eCollection 2021 Jan 22.
Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.
技术发展彻底改变了对植物基因型和表型的测量，导致大型复杂数据集的常规生产。这使得人们更加努力地从这些测量中提取意义，并整合各种数据集。与此同时，机器学习迅速发展，现在广泛应用于一般科学领域，尤其是植物基因分型和表型分析。在这里，我们回顾机器学习在植物科学和植物育种背景下的应用。我们专注于从生化水平到产量的不同表型水平的分析，以及将基因型与这些表型联系起来的分析。通过这种方式，我们说明了机器学习如何提供一套方法，使研究人员能够在相关植物数据中找到有意义的模式。
