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AI-driven irrigation systems for sustainable water management: A systematic review and meta-analytical insights
- Truvace record
- TRV-2026-2738raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- 086551efb7fe…check it
This review systematically examines recent advancements in AI-driven irrigation systems and their role in achieving sustainable water management under climate-resilient agricultural practices. By integrating machine learning algorithms, computer vision, and IoT-based sensors, these autonomous systems enable real-time soil–plant monitoring, adaptive water scheduling, and resource optimization across diverse agro-climatic contexts. Drawing upon a broad range of peer-reviewed experimental and modeling studies publi…
The gain
A systematic review and meta-analysis of experimental and modeling studies published from 2018 to 2025 found AI-driven irrigation cut water use by 30–50% and raised crop yields by 20–30%.
What this doesn’t fix
The authors say validation still needs to be expanded across diverse crop types and regional settings, and that standardization frameworks and cross-platform compatibility are still needed.
Sources
1 on file- Smart Agricultural Technologydoi:10.1016/j.atech.2025.100982
Cite this record
v1Truvace Impact Record TRV-2026-2738, v1: “AI-driven irrigation systems for sustainable water management: A systematic review and meta-analytical insights.” Truvace, 2026-11-12. /record/TRV-2026-2738 (accessed on the day you copy it). sha256 086551efb7fed9bd…
Change history
1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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