Predictive Yield Mapping

Predictive Yield Mapping is a geospatial modeling framework that integrates historical yield data, environmental variables, and machine learning algorithms to generate pre-harvest spatial predictions of crop productivity, distinguishing itself from conventional yield mapping by emphasizing forecast accuracy and spatial resolution for proactive harvest planning.

41

Publications

2.2K

Citations

201

Authors

104

Institutions

Publications per year

2017–2026

32

Authors

201

  1. DM
    David Makowski

    AgroParisTech, France

    3 publications · 166 citations

    3

    h-index

  2. CC
    Carlos Campillo

    Government of Extremadura, Spain

    2 publications · 38 citations

    2

    h-index

  3. YR
    Yannik Roell

    Aarhus University, Denmark

    2 publications · 69 citations

    2

    h-index

  4. RF
    R. Fortes

    Centro Tecnológico Agroalimentario de Extremadura, Spain

    2 publications · 38 citations

    2

    h-index

  5. RP
    Rustam Paringer

    Image Processing Systems Institute, Russia

    2 publications · 89 citations

    2

    h-index

1–5 of 201

Venues

  1. Agricultural and Forest Meteorology

    4 publications · 217 citations

    4

    h-index

  2. Remote Sensing

    4 publications · 156 citations

    4

    h-index

  3. Computers and Electronics in Agriculture

    4 publications · 156 citations

    3

    h-index

  4. Remote Sensing of Environment

    2 publications · 166 citations

    2

    h-index

  5. Spanish Journal of Agricultural Research

    SJAR · 1 publications · 24 citations

    1

    h-index

1–5