Towards efficient irrigation management at field scale using new technologies: A systematic literature review
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DOI: 10.1016/j.agwat.2024.108758
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- Bwambale, Erion & Abagale, Felix K. & Anornu, Geophrey K., 2022. "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, Elsevier, vol. 260(C).
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- Alibabaei, Khadijeh & Gaspar, Pedro D. & Assunção, Eduardo & Alirezazadeh, Saeid & Lima, Tânia M., 2022. "Irrigation optimization with a deep reinforcement learning model: Case study on a site in Portugal," Agricultural Water Management, Elsevier, vol. 263(C).
- Shao, Guomin & Han, Wenting & Zhang, Huihui & Liu, Shouyang & Wang, Yi & Zhang, Liyuan & Cui, Xin, 2021. "Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices," Agricultural Water Management, Elsevier, vol. 252(C).
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- Han, Ming & Zhao, Chengyi & Šimůnek, Jirka & Feng, Gary, 2015. "Evaluating the impact of groundwater on cotton growth and root zone water balance using Hydrus-1D coupled with a crop growth model," Agricultural Water Management, Elsevier, vol. 160(C), pages 64-75.
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- Parra-López, Carlos & Ben Abdallah, Saker & Garcia-Garcia, Guillermo & Hassoun, Abdo & Trollman, Hana & Jagtap, Sandeep & Gupta, Sumit & Aït-Kaddour, Abderrahmane & Makmuang, Sureerat & Carmona-Torres, 2025. "Digital technologies for water use and management in agriculture: Recent applications and future outlook," Agricultural Water Management, Elsevier, vol. 309(C).
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