Kumari et al. (2025) Geospatial and machine learning-based mapping and analysis for agricultural sustainability

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-05
• Authors: Rashmi Kumari, Kuldeep Chaurasia, Ghazaala Yasmin, Deepak Kumar, Subhranil Das
• DOI: 10.1016/b978-0-443-34113-7.00012-2

Research Groups

• School of Computer Science Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, India
• Department of Computer Science and Engineering/Information Technology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
• Atmospheric Science Research Center, University at Albany—State University of New York, Albany, NY, United States
• School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

Short Summary

This chapter explores the application of geospatial technologies and machine learning for enhancing agricultural sustainability, highlighting their role in precision farming, land use mapping, and environmental monitoring to address global food security challenges.

Objective

• To investigate how geospatial technologies and machine learning can be effectively utilized to optimize agricultural practices and enhance sustainability in the face of global challenges like climate change, population growth, and resource scarcity.

Study Configuration

• Spatial Scale: Conceptual, focusing on agricultural landscapes globally.
• Temporal Scale: Conceptual, focusing on continuous monitoring and change detection in agricultural parameters.

Methodology and Data

• Models used: Machine learning (ML) algorithms (specific types not detailed in this introductory text).
• Data sources: Satellite images, remote sensing, geographic information systems (GIS), unmanned aerial vehicles (UAVs).

Main Results

• Geospatial technologies (satellite images, remote sensing, GIS, UAVs) combined with machine learning are identified as powerful tools for transforming traditional agriculture into efficient industrial applications.
• These tools enable high-precision monitoring of crops, soils, and environmental conditions.
• Key applications include precision farming (e.g., monitoring soil moisture levels, tracking vegetation indices), land use and soil mapping, identifying optimal land use practices, monitoring soil quality, assessing erosion risks, and climate monitoring.

Contributions

• This chapter synthesizes the current state and potential of geospatial technologies and machine learning as transformative tools for achieving agricultural sustainability.
• It provides a comprehensive overview of their diverse applications in precision farming, resource management, and environmental monitoring, addressing critical challenges in global food security.

Funding

• Not specified in the provided text.

Citation

@article{Kumari2025Geospatial,
  author = {Kumari, Rashmi and Chaurasia, Kuldeep and Yasmin, Ghazaala and Kumar, Deepak and Das, Subhranil},
  title = {Geospatial and machine learning-based mapping and analysis for agricultural sustainability},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-34113-7.00012-2},
  url = {https://doi.org/10.1016/b978-0-443-34113-7.00012-2}
}