Deep Learning in Image-Based Plant Phenotyping

Katherine M. Murphy; Ella Ludwig; Jorge Gutierrez; Malia A. Gehan

doi:10.1146/annurev-arplant-070523-042828

journal article Open Access Jul 22, 2024

Deep Learning in Image-Based Plant Phenotyping

Katherine M. Murphy

Ella Ludwig Jorge Gutierrez Malia A. Gehan

Annual Review of Plant Biology Vol. 75 No. 1 pp. 771-795 · Annual Reviews

View at Publisher Save 10.1146/annurev-arplant-070523-042828

Abstract

A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.

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References

Details

Published: Jul 22, 2024
Vol/Issue: 75(1)
Pages: 771-795
License: View

Authors

K

Katherine M. Murphy

Donald Danforth Plant Science Center, St. Louis, Missouri, USA; email: mgehan@danforthcenter.org

E

Ella Ludwig

Donald Danforth Plant Science Center, St. Louis, Missouri, USA; email: mgehan@danforthcenter.org

J

Jorge Gutierrez

Donald Danforth Plant Science Center, St. Louis, Missouri, USA; email: mgehan@danforthcenter.org

M

Malia A. Gehan

Donald Danforth Plant Science Center, St. Louis, Missouri, USA; email: mgehan@danforthcenter.org

Cite This Article

Katherine M. Murphy, Ella Ludwig, Jorge Gutierrez, et al. (2024). Deep Learning in Image-Based Plant Phenotyping. Annual Review of Plant Biology, 75(1), 771-795. https://doi.org/10.1146/annurev-arplant-070523-042828

Deep Learning in Image-Based Plant Phenotyping

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