Data-driven Irrigation Control

Data-Driven Irrigation Control is a methodology in agricultural water management that uses empirical data from sensors, weather forecasts, and crop monitoring systems to dynamically regulate irrigation schedules and amounts, distinguishing itself from rule-based or fixed-timer approaches through its reliance on real-time, measurable environmental inputs and analytical models to optimize water use efficiency.

22

Publications

1.2K

Citations

84

Authors

55

Institutions

Publications per year

2017–2026

19

Authors

84

  1. GK
    Geophrey K. Anornu

    Kwame Nkrumah University of Science and Technology, Ghana

    2 publications · 97 citations

    2

    h-index

  2. CS
    Chao Shang

    Tsinghua University, China

    2 publications · 133 citations

    2

    h-index

  3. AE
    Abiodun Emmanuel Abioye

    The Federal Polytechnic, Ado-Ekiti, Nigeria

    2 publications · 265 citations

    2

    h-index

  4. WC
    Wei‐Han Chen

    Cornell University, United States

    2 publications · 133 citations

    2

    h-index

  5. SK
    Su Ki Ooi

    The University of Melbourne, Australia

    2 publications · 82 citations

    2

    h-index

1–5 of 84

Venues

  1. Smart Agricultural Technology

    3 publications · 108 citations

    3

    h-index

  2. Control Engineering Practice

    2 publications · 99 citations

    2

    h-index

  3. Information

    1 publications · 34 citations

    1

    h-index

  4. Cell & Cellular Life Sciences Journal

    CCLSJ · 1 publications · 14 citations

    1

    h-index

  5. Agricultural Water Management

    1 publications · 72 citations

    1

    h-index

1–5