Digital twin-enabled intelligent irrigation-drainage system for precision water-salt management in saline agroecosystems

Soil salinization and freshwater scarcity are widely recognized as persistent constraints on sustainable agricultural intensification, especially in arid and semi-arid regions. This study presents a targeted approach by developing an integrated intelligent irrigation-drainage platform that combines Internet of Things (IoT)-based multi-layer environmental sensing, Long Short-Term Memory (LSTM) deep learning for predictive modeling, and digital twin-assisted management. A closed-loop irrigation-drainage infrastructure was concurrently implemented, incorporating multi-source water reuse pipelines, subsurface drainage networks, and photovoltaic-powered units for brackish water purification. Field deployment in saline-affected agricultural plots in northwestern China enabled high-frequency monitoring of soil moisture, temperature, and salinity dynamics under complex soil stratification conditions. The Long Short-Term Memory (LSTM) model demonstrated high predictive accuracy (R² = 0.97 for soil electrical conductivity and R² = 0.92 for water content), while feature importance analysis identified soil moisture, temperature, and groundwater depth were the dominant factors driving water-salt interactions. Coordinated irrigation and drainage scheduling significantly reduced soil salinity across permeable soil profiles, while closed-loop water reuse strategies enhanced water-use efficiency without increasing salinity risks. This study highlights the potential of intelligent irrigation-drainage systems integrating real-time monitoring, predictive modeling, and closed-loop water reuse to enhance salt leaching efficiency, optimize water utilization, and stabilize crop production across heterogeneous saline agroecosystems. • A digital twin-based irrigation-drainage system was developed for saline farmlands. • Real-time monitoring and Long Short-Term Memory (LSTM) prediction enabled adaptive water-salt control. • A closed-loop reuse system improved leaching efficiency and reduced salinity. • Field experiments demonstrated significant salt reduction across soil profiles.

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Publication Details

Journal
Agricultural Water Management
Published
2025-11-06
DOI
https://doi.org/10.1016/j.agwat.2025.109957
Citations
2
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
3.50
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article

Digital twin-enabled intelligent irrigation-drainage system for precision water-salt management in saline agroecosystems

Chuansong Zhang, Feng Xiong, Mei Qin, Guorong Ma et al.
2 citations
Agricultural Water Management
Smart Agriculture and AI
3.50
article

Digital twin-enabled intelligent irrigation-drainage system for precision water-salt management in saline agroecosystems

Chuansong Zhang, Feng Xiong, Mei Qin, Guorong Ma, Yongcheng Ma, Peijie Li, Kepeng Feng
article en
2 citations

Abstract

Soil salinization and freshwater scarcity are widely recognized as persistent constraints on sustainable agricultural intensification, especially in arid and semi-arid regions. This study presents a targeted approach by developing an integrated intelligent irrigation-drainage platform that combines Internet of Things (IoT)-based multi-layer environmental sensing, Long Short-Term Memory (LSTM) deep learning for predictive modeling, and digital twin-assisted management. A closed-loop irrigation-drainage infrastructure was concurrently implemented, incorporating multi-source water reuse pipelines, subsurface drainage networks, and photovoltaic-powered units for brackish water purification. Field deployment in saline-affected agricultural plots in northwestern China enabled high-frequency monitoring of soil moisture, temperature, and salinity dynamics under complex soil stratification conditions. The Long Short-Term Memory (LSTM) model demonstrated high predictive accuracy (R² = 0.97 for soil electrical conductivity and R² = 0.92 for water content), while feature importance analysis identified soil moisture, temperature, and groundwater depth were the dominant factors driving water-salt interactions. Coordinated irrigation and drainage scheduling significantly reduced soil salinity across permeable soil profiles, while closed-loop water reuse strategies enhanced water-use efficiency without increasing salinity risks. This study highlights the potential of intelligent irrigation-drainage systems integrating real-time monitoring, predictive modeling, and closed-loop water reuse to enhance salt leaching efficiency, optimize water utilization, and stabilize crop production across heterogeneous saline agroecosystems. • A digital twin-based irrigation-drainage system was developed for saline farmlands. • Real-time monitoring and Long Short-Term Memory (LSTM) prediction enabled adaptive water-salt control. • A closed-loop reuse system improved leaching efficiency and reduced salinity. • Field experiments demonstrated significant salt reduction across soil profiles.

Agricultural Water ManagementVol. 322
Shihezi University (CN), Ningxia University (CN), Ningxia Water Conservancy (CN)
Openalex Percentile: Top 7%
Smart Agriculture and AI
3.50
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