Digital technologies for water use and management in agriculture: Recent applications and future outlook
Parra-López, Carlos ; Ben Abdallah, Saker ; Garcia-Garcia, Guillermo ; Hassoun, Abdo ; Trollman, Hana ; Jagtap, Sandeep LU
; Gupta, Sumit
; Aït-Kaddour, Abderrahmane
; Makmuang, Sureerat
and Carmona-Torres, Carmen
(2025)
In Agricultural Water Management
309.
- Abstract
- This article provides a comprehensive overview of digital technologies for water use and management in agriculture, examining recent applications and future prospects. It examines key water-related challenges - scarcity, pollution, inefficient use and climate change - and shows how various digital technologies such as Remote Sensing, Artificial Intelligence, the Internet of Things, Big Data, Robotics, Smart Sensors and Blockchain can help address them. The review finds that these technologies offer significant potential for improving water management practices, with Remote Sensing and Artificial Intelligence emerging as the most versatile and widely adopted. Efficient irrigation strategies appear to be the most common application across... (More)
- This article provides a comprehensive overview of digital technologies for water use and management in agriculture, examining recent applications and future prospects. It examines key water-related challenges - scarcity, pollution, inefficient use and climate change - and shows how various digital technologies such as Remote Sensing, Artificial Intelligence, the Internet of Things, Big Data, Robotics, Smart Sensors and Blockchain can help address them. The review finds that these technologies offer significant potential for improving water management practices, with Remote Sensing and Artificial Intelligence emerging as the most versatile and widely adopted. Efficient irrigation strategies appear to be the most common application across technologies. Digital solutions significantly reduce water wastage, help identify pollution hotspots, and improve overall water resource management. For example, remote sensing-based approaches (e.g. UAV-mounted multispectral cameras) can accurately monitor soil moisture to optimise irrigation scheduling, while AI-driven models (e.g. random forest or neural networks) can predict groundwater recharge or forecast rainfall events. However, several barriers to widespread adoption are identified, including high implementation costs, lack of technical expertise, data management challenges, and infrastructure and connectivity constraints. The study concludes by suggesting priorities for future research and development, highlighting the need for integrated technological solutions, improved accessibility and affordability, improved efficiency and sustainability, improved water quality, enhanced data management capabilities, and strategies to address emerging concerns such as cybersecurity and the environmental impact of digital technologies themselves. This review aims to inform future research, policy and practice in agricultural water management and support the development of more productive, resilient and sustainable agricultural systems. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/a6cb8731-0f14-4e9b-96f9-556075d9296a
- author
- Parra-López, Carlos
; Ben Abdallah, Saker
; Garcia-Garcia, Guillermo
; Hassoun, Abdo
; Trollman, Hana
; Jagtap, Sandeep
LU
; Gupta, Sumit
; Aït-Kaddour, Abderrahmane
; Makmuang, Sureerat
and Carmona-Torres, Carmen
- organization
- publishing date
- 2025-03-31
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Agricultural Water Management
- volume
- 309
- article number
- 109347
- pages
- 21 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85216577603
- ISSN
- 1873-2283
- DOI
- 10.1016/j.agwat.2025.109347
- language
- English
- LU publication?
- yes
- id
- a6cb8731-0f14-4e9b-96f9-556075d9296a
- date added to LUP
- 2025-02-02 08:28:38
- date last changed
- 2025-04-04 14:09:01
@article{a6cb8731-0f14-4e9b-96f9-556075d9296a,
abstract = {{This article provides a comprehensive overview of digital technologies for water use and management in agriculture, examining recent applications and future prospects. It examines key water-related challenges - scarcity, pollution, inefficient use and climate change - and shows how various digital technologies such as Remote Sensing, Artificial Intelligence, the Internet of Things, Big Data, Robotics, Smart Sensors and Blockchain can help address them. The review finds that these technologies offer significant potential for improving water management practices, with Remote Sensing and Artificial Intelligence emerging as the most versatile and widely adopted. Efficient irrigation strategies appear to be the most common application across technologies. Digital solutions significantly reduce water wastage, help identify pollution hotspots, and improve overall water resource management. For example, remote sensing-based approaches (e.g. UAV-mounted multispectral cameras) can accurately monitor soil moisture to optimise irrigation scheduling, while AI-driven models (e.g. random forest or neural networks) can predict groundwater recharge or forecast rainfall events. However, several barriers to widespread adoption are identified, including high implementation costs, lack of technical expertise, data management challenges, and infrastructure and connectivity constraints. The study concludes by suggesting priorities for future research and development, highlighting the need for integrated technological solutions, improved accessibility and affordability, improved efficiency and sustainability, improved water quality, enhanced data management capabilities, and strategies to address emerging concerns such as cybersecurity and the environmental impact of digital technologies themselves. This review aims to inform future research, policy and practice in agricultural water management and support the development of more productive, resilient and sustainable agricultural systems.}},
author = {{Parra-López, Carlos and Ben Abdallah, Saker and Garcia-Garcia, Guillermo and Hassoun, Abdo and Trollman, Hana and Jagtap, Sandeep and Gupta, Sumit and Aït-Kaddour, Abderrahmane and Makmuang, Sureerat and Carmona-Torres, Carmen}},
issn = {{1873-2283}},
language = {{eng}},
month = {{03}},
publisher = {{Elsevier}},
series = {{Agricultural Water Management}},
title = {{Digital technologies for water use and management in agriculture: Recent applications and future outlook}},
url = {{https://lup.lub.lu.se/search/files/207592624/1-s2.0-S0378377425000617-main.pdf}},
doi = {{10.1016/j.agwat.2025.109347}},
volume = {{309}},
year = {{2025}},
}