Irrigation Demand Forecasting
Irrigation Demand Forecasting is a predictive modeling approach that estimates future crop water requirements by integrating environmental, agronomic, and climatic data to optimize irrigation scheduling in precision agriculture systems.
86
Publications
3.5K
Citations
297
Authors
130
Institutions
Publications per year
2017–2026
38
86
IoT based Smart Agriculture using Machine Learning
Kasara Sai Pratyush Reddy, Y. Mohana Roopa, Kovvada Rajeev L.N. et al. · 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) · 2020 · 191 citations
Engineering, Agricultural Engineering, Internet Of Things +16
Forrest Melton, Lee Johnson, C.P. Lund et al. · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2012 · 138 citations
Agriculture, Agronomy, Hydrology +17
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130
University of Córdoba
Córdoba, Spain
6 h-index
18 publications
687 citations

The University of Melbourne
Melbourne, Australia
4 h-index
10 publications
220 citations

Beijing Normal University
Beijing, China
3 h-index
3 publications
136 citations

Universidad de Huelva
Huelva, Spain
3 h-index
5 publications
512 citations
United Arab Emirates University
Al Ain, United Arab Emirates
3 h-index
4 publications
192 citations
University of Technology Malaysia
Johor Bahru, Malaysia
3 h-index
4 publications
149 citations

Bureau of Meteorology
Melbourne, Australia
2 h-index
2 publications
68 citations
Wuhan University
Wuhan, China
2 h-index
6 publications
390 citations
1–8 of 130
11 publications · 476 citations
11
h-index
6 publications · 262 citations
6
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3 publications · 115 citations
3
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2 publications · 91 citations
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2 publications · 71 citations
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