Deep Learning-based Phenotyping

Deep Learning-Based Phenotyping is an imaging-based phenotyping approach that leverages deep neural networks to automatically extract and classify phenotypic traits from high-dimensional image data, distinguishing itself from conventional methods through its capacity for hierarchical feature learning and end-to-end model optimization.

349

Publications

20.2K

Citations

2.5K

Authors

1K

Institutions

Publications per year

2017–2026

332

Authors

2.5K

  1. IS
    Ian Stavness

    University of Saskatchewan, Canada

    6 publications · 729 citations

    7

    h-index

  2. TP
    Tony Pridmore

    University of Nottingham, United Kingdom

    6 publications · 661 citations

    6

    h-index

  3. MP
    Michael P. Pound

    University of Nottingham, United Kingdom

    5 publications · 715 citations

    6

    h-index

  4. MV
    Mario Valerio Giuffrida

    University of Edinburgh, United Kingdom

    5 publications · 311 citations

    5

    h-index

  5. SA
    Sotirios A. Tsaftaris

    University of Edinburgh, United Kingdom

    5 publications · 311 citations

    5

    h-index

1–5 of 2.5K

Venues

  1. Frontiers in Plant Science

    15 publications · 1.1K citations

    14

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 17 publications · 484 citations

    13

    h-index

  3. Nature Communications

    13 publications · 1.5K citations

    13

    h-index

  4. Scientific Reports

    10 publications · 833 citations

    11

    h-index

  5. Plant Phenomics

    9 publications · 371 citations

    9

    h-index

1–5