Omar et al. (2025) Deep learning and geospatial technology-based decision support systems for smart agricultural and irrigation applications

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-05
• Authors: Padam Jee Omar, Pankaj Kumar Gupta, Manvendra Singh Chauhan, Kundan Meshram
• DOI: 10.1016/b978-0-443-34113-7.00004-3

Research Groups

• Department of Civil Engineering, UIET Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India
• Faculty of Environment, University of Waterloo, Waterloo, ON, Canada
• Department of Civil Engineering, Guru Ghasidas Vishwavidyalaya Bilaspur (A Central University), Bilaspur, Chhattisgarh, India

Short Summary

This paper introduces the critical need for advanced agricultural practices to address global challenges like food security and climate change, proposing deep learning and geospatial technologies as a viable solution for developing smart agricultural and irrigation decision support systems.

Objective

• To explore the application of deep learning and geospatial technologies for developing decision support systems in smart agriculture and irrigation.
• To address the limitations of conventional agricultural practices and enhance resource efficiency and sustainability in the face of growing population and climate change impacts.

Study Configuration

• Spatial Scale: Conceptual framework for applications ranging from farm-level to regional scales.
• Temporal Scale: Conceptual framework for real-time or near real-time decision support systems.

Methodology and Data

• Models used: Deep learning (general category).
• Data sources: Geospatial technologies (general category, implying satellite, GIS, and other spatial data).

Main Results

The provided text is an introductory chapter and does not present specific results from a study. It outlines the problem statement and the potential of deep learning and geospatial technologies as a solution.

Contributions

• Highlights the urgent need for advanced agricultural solutions to manage limited resources and adapt to climate change.
• Proposes deep learning and geospatial technologies as a cutting-edge approach to overcome the shortcomings of conventional agricultural practices.
• Lays the conceptual groundwork for developing smart agricultural and irrigation decision support systems.

Funding

No funding information is provided in the excerpt.

Citation

@article{Omar2025Deep,
  author = {Omar, Padam Jee and Gupta, Pankaj Kumar and Chauhan, Manvendra Singh and Meshram, Kundan},
  title = {Deep learning and geospatial technology-based decision support systems for smart agricultural and irrigation applications},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-34113-7.00004-3},
  url = {https://doi.org/10.1016/b978-0-443-34113-7.00004-3}
}