Machine learning in plant science and plant breeding

Summary 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.

• Publication year

  2020

• Venue

  iScience

• Publication date

  2020-12-05

• Fields of study

  Biology, Agricultural and Food Sciences, Medicine, Computer Science

• Identifiers
  DOI 10.1016/j.isci.2020.101890PMID 33364579PMCID 7750553
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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