Machine Learning Yield Forecasting

Machine Learning Yield Forecasting is a computational methodology that employs machine learning algorithms to predict agricultural yield by learning patterns from historical and environmental data, distinguishing itself from traditional statistical forecasting through its capacity to model nonlinear, high-dimensional relationships.

767

Publications

53K

Citations

2.8K

Authors

1K

Institutions

Publications per year

2017–2026

655

Authors

2.8K

  1. GN
    Gniewko Niedbała

    University of Life Sciences in Poznań, Poland

    9 publications · 400 citations

    9

    h-index

  2. DL
    De Li Liu

    UNSW Sydney, Australia

    7 publications · 719 citations

    7

    h-index

  3. ZZ
    Zhou Zhang

    University of Wisconsin–Madison, United States

    7 publications · 784 citations

    7

    h-index

  4. BW
    Bin Wang

    UNSW Sydney, Australia

    6 publications · 593 citations

    6

    h-index

  5. BD
    Bappa Das

    Central Coastal Agricultural Research Institute, India

    6 publications · 290 citations

    6

    h-index

1–5 of 2.8K

Venues

  1. Computers and Electronics in Agriculture

    55 publications · 6.3K citations

    38

    h-index

  2. Remote Sensing

    19 publications · 1.8K citations

    19

    h-index

  3. Agricultural and Forest Meteorology

    19 publications · 3K citations

    19

    h-index

  4. Agriculture

    17 publications · 781 citations

    16

    h-index

  5. Agronomy

    16 publications · 837 citations

    15

    h-index

1–5