Chatrabhuj et al. (2025) Geo-artificial intelligence for smart irrigation management systems

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-05
• Authors: Chatrabhuj, Kundan Meshram, Padam Jee Omar
• DOI: 10.1016/b978-0-443-34113-7.00007-9

Research Groups

• Department of Civil Engineering, Guru Ghasidas Vishwavidyalaya, Bilaspur, Chhattisgarh, India
• Department of Civil Engineering, UIET, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India

Short Summary

This chapter introduces Geo-artificial intelligence (Geo-AI) as an integration of geospatial technologies with AI for analyzing and processing geography-based data. It highlights Geo-AI's application in developing smart systems for environmental management, planning, and particularly, the optimization of irrigation management systems.

Objective

• To explore the foundational concepts and applications of Geo-artificial intelligence (Geo-AI) for the development and enhancement of smart irrigation management systems.

Study Configuration

• Spatial Scale: Conceptual, focusing on agricultural landscapes and spatial data analysis broadly.
• Temporal Scale: Conceptual, addressing real-time processing and time-enabled data within Geo-AI frameworks.

Methodology and Data

• Models used: Machine learning, deep learning, and neural networks are mentioned as core methods within Geo-AI.
• Data sources: Geography-based data samples, spatial data, diverse data sources, Geographic Information Systems (GIS), and remote sensing (RS).

Main Results

• The provided text is an introductory chapter and does not present specific experimental or analytical results. It defines Geo-AI as a state-of-the-art approach combining GIS, RS, and AI for complex spatial tasks, with applications including irrigation system optimization, yield prediction, and crop health monitoring.

Contributions

• This chapter synthesizes the integration of geospatial technologies with artificial intelligence to define Geo-AI.
• It positions Geo-AI as a crucial, state-of-the-art approach for addressing challenges in spatial data analysis and real-time processing within agricultural management, specifically for smart irrigation systems.

Funding

• No funding information is provided in the excerpt.

Citation

@article{Chatrabhuj2025Geoartificial,
  author = {Chatrabhuj and Meshram, Kundan and Omar, Padam Jee},
  title = {Geo-artificial intelligence for smart irrigation management systems},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-34113-7.00007-9},
  url = {https://doi.org/10.1016/b978-0-443-34113-7.00007-9}
}