Semantic Scholar Open Access 2020 205 sitasi

Machine learning in plant science and plant breeding

A. V. van Dijk G. Kootstra W. Kruijer D. de Ridder

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Medicine Biology

Penulis (4)

A. V. van Dijk

G. Kootstra

W. Kruijer

D. de Ridder

Format Sitasi

APA MLA BibTeX

Dijk, A.V.v., Kootstra, G., Kruijer, W., Ridder, D.d. (2020). Machine learning in plant science and plant breeding. https://doi.org/10.1016/j.isci.2020.101890

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.isci.2020.101890

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 205×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.isci.2020.101890
Akses: Open Access ✓