Machine Learning Yield Modeling

Machine Learning Yield Modeling is a computational approach within yield estimation that employs machine learning algorithms to predict production outcomes by learning complex patterns from empirical data, distinguishing itself from traditional statistical or rule-based models through its adaptive, data-driven modeling capability.

424

Publications

24.1K

Citations

1.5K

Authors

617

Institutions

Publications per year

2017–2026

351

Authors

1.5K

  1. GN
    Gniewko Niedbała

    University of Life Sciences in Poznań, Poland

    5 publications · 355 citations

    6

    h-index

  2. CC
    Chen–Fu Chien

    National Tsing Hua University, Taiwan

    5 publications · 643 citations

    5

    h-index

  3. MP
    Magdalena Piekutowska

    Uniwersytet Pomorski w Słupsku, Poland

    3 publications · 97 citations

    4

    h-index

  4. TC
    Toly Chen

    Feng Chia University, Taiwan

    4 publications · 60 citations

    4

    h-index

  5. RA
    Rubby Aworka

    Université Internationale de Casablanca, Morocco

    3 publications · 294 citations

    3

    h-index

1–5 of 1.5K

Venues

  1. Computers and Electronics in Agriculture

    20 publications · 3K citations

    20

    h-index

  2. Agriculture

    9 publications · 728 citations

    10

    h-index

  3. 9

    h-index

  4. IEEE Access

    6 publications · 490 citations

    7

    h-index

  5. Scientific Reports

    5 publications · 178 citations

    6

    h-index

1–5