Haraz et al. (2025) Towards Smart Irrigation: Architecture of an Irrigation System Based on Artificial Intelligence

Identification

• Journal: Lecture notes in networks and systems
• Year: 2025
• Date: 2025-11-11
• Authors: Zakariae Haraz, Reda Yaagoubi, Mourad Bouziani, Sara Sahraoui, Lahcen Kenny
• DOI: 10.1007/978-3-032-02312-4_18

Research Groups

• School of Geomatic Sciences and Surveying Engineering, Agronomic and Veterinary Institute Hassan II, Rabat, Morocco
• Departement of Horticulture, CHA-Agronomic and Veterinary Institute Hassan II, Agadir, Morocco

Short Summary

This paper proposes an innovative four-layer architecture for an intelligent irrigation system, leveraging Internet of Things (IoT) and Artificial Intelligence (AI) technologies to optimize water resource management and maximize agricultural yield in drylands, particularly in Morocco, amidst climate change and water scarcity.

Objective

• To design and present a four-layer intelligent irrigation system architecture that integrates IoT sensors for real-time data collection and AI algorithms for data processing and forecasting, aiming to optimize agricultural water use, reduce waste, and enhance crop yield.

Study Configuration

• Spatial Scale: Agricultural systems, with a specific focus on drylands and the context of Morocco.
• Temporal Scale: Real-time data collection and continuous management for dynamic irrigation optimization.

Methodology and Data

• Models used: Artificial intelligence algorithms for data processing and generating forecasts on key indicators such as plant water stress and evapotranspiration.
• Data sources: Real-time data collected by IoT sensors, including soil moisture, temperature, and precipitation.

Main Results

• An innovative four-layer intelligent irrigation system architecture is proposed, comprising detection, network, data storage/processing (AI), and application layers.
• The system utilizes IoT sensors for real-time environmental data collection and AI algorithms for accurate forecasting of plant water stress and evapotranspiration.
• It provides actionable irrigation recommendations to farmers through an intuitive web Geographic Information System (GIS) platform.
• The architecture is designed to significantly improve irrigation practices in drylands by optimizing water resource use, reducing waste, and maximizing agricultural yield.

Contributions

• Proposes a novel, comprehensive four-layer IoT-AI architecture specifically tailored for smart irrigation in dryland agricultural systems.
• Integrates real-time sensor data with advanced artificial intelligence for predictive analytics, enabling more precise and efficient irrigation scheduling.
• Offers a sustainable and resilient solution to address water scarcity and climate challenges in modern agriculture, promoting increased agricultural yield while conserving water resources.

Funding

• Ministry of Higher Education, Scientific Research, and Innovation (Morocco)
• Digital Development Agency of Morocco (DDA)
• National Center for Scientific and Technical Research of Morocco (CNRST)
• Project reference: Alkhawarizmi/2020/17

Citation

@article{Haraz2025Towards,
  author = {Haraz, Zakariae and Yaagoubi, Reda and Bouziani, Mourad and Sahraoui, Sara and Kenny, Lahcen},
  title = {Towards Smart Irrigation: Architecture of an Irrigation System Based on Artificial Intelligence},
  journal = {Lecture notes in networks and systems},
  year = {2025},
  doi = {10.1007/978-3-032-02312-4_18},
  url = {https://doi.org/10.1007/978-3-032-02312-4_18}
}