Das et al. (2025) Integration of geospatial technology and machine learning for precision agriculture

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-05
• Authors: Siddhartha Das, Pradipta Banerjee, Souptik Karmakar
• DOI: 10.1016/b978-0-443-34113-7.00010-9

Research Groups

• Department of Plant Pathology, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi, Odisha, India
• McGowan Institute for Regenerative Medicine, Department of Surgery, University of Pittsburgh, Pittsburgh, PA, United States
• Department of Seed Science & Technology, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India

Short Summary

This chapter introduces the significant potential of integrating geospatial technology (GIS, GPS, remote sensing) with machine learning frameworks to enhance analysis and decision-making in precision agriculture. It highlights how this synergy leverages spatial context and computational power to gain insights into crop growth, soil properties, and environmental factors.

Objective

• To explore and articulate the transformative potential of integrating geospatial technology and machine learning for advanced geospatial examination and decision-making in agricultural research and management, particularly for understanding crop-soil-environment relationships.

Study Configuration

• Spatial Scale: Not explicitly defined in this introductory text, but implied to be relevant for agricultural fields and regional applications in precision agriculture.
• Temporal Scale: Not explicitly defined in this introductory text, but implied to involve continuous or periodic monitoring for agricultural insights.

Methodology and Data

• Models used: General "machine learning frameworks" are discussed as a potent method for gaining insights from complex datasets. No specific models are detailed in this introductory text.
• Data sources: "Spatial data" (collected, stored, handled, analyzed, and visualized by GIS), "big and complicated datasets" (for machine learning), and data from "remote sensing" are mentioned as key inputs.

Main Results

This introductory chapter does not present specific experimental results but rather outlines the conceptual framework and potential benefits of integrating geospatial technology and machine learning for precision agriculture.

Contributions

This article contributes by synthesizing the synergistic advantages of combining geospatial technology's spatial context and domain expertise with machine learning's computational power and predictive potential, thereby outlining a transformative approach for agricultural analysis and decision-making.

Funding

Not mentioned in the provided text.

Citation

@article{Das2025Integration,
  author = {Das, Siddhartha and Banerjee, Pradipta and Karmakar, Souptik},
  title = {Integration of geospatial technology and machine learning for precision agriculture},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-34113-7.00010-9},
  url = {https://doi.org/10.1016/b978-0-443-34113-7.00010-9}
}