Automated Image Recognition

Automated Image Recognition is a computational method that enables the identification and classification of pollinator species from digital images through algorithmic analysis of visual features, distinguishing it from manual or non-automated techniques by its reliance on machine learning models to perform recognition tasks without human intervention.

844

Publications

46.4K

Citations

3.6K

Authors

1.3K

Institutions

Publications per year

2017–2026

564

Authors

3.6K

  1. ‪Y
    ‪Keiji Yanai‬

    University of Electro-Communications, Japan

    10 publications · 597 citations

    10

    h-index

  2. MI
    Makoto Iida

    The University of Tokyo, Japan

    4 publications · 131 citations

    4

    h-index

  3. TN
    Takeshi Naemura

    The University of Tokyo, Japan

    4 publications · 131 citations

    4

    h-index

  4. RY
    Ryota Yoshihashi

    The University of Tokyo, Japan

    4 publications · 131 citations

    4

    h-index

  5. HF
    Hiroshi Fujita

    Gifu University, Japan

    3 publications · 48 citations

    3

    h-index

1–5 of 3.6K

Venues

  1. Scientific Reports

    23 publications · 1.9K citations

    22

    h-index

  2. arXiv (Cornell University)

    Repository · 19 publications · 603 citations

    14

    h-index

  3. IEEE Access

    14 publications · 1K citations

    13

    h-index

  4. Pattern Recognition

    13 publications · 1.1K citations

    12

    h-index

  5. Sensors

    11 publications · 446 citations

    11

    h-index

1–5