Singh et al. (2025) Crop health monitoring using geospatial methods and deep learning

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-05
• Authors: Yadvendra Pratap Singh, Jayesh Gangrade, Mukund Pratap Singh, Surendra Solanki, Shishir Singh Chauhan
• DOI: 10.1016/b978-0-443-34113-7.00006-7

Research Groups

• Department of Artificial Intelligence & Machine Learning, School of Computer Science & Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India
• School of Computer Science and Engineering and Technology, Bennett University, Greater Noida, Uttar Pradesh, India
• Department of Computer Science and Engineering, School of Computer Science & Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

Short Summary

This paper introduces the critical role of integrating deep learning with geospatial technologies (GIS, remote sensing, GPS) to revolutionize crop health monitoring and management, aiming to overcome the limitations of traditional field surveys.

Objective

• To explore and advocate for the application of geospatial methods and deep learning techniques to enhance crop health monitoring, precision farming, and resource utilization in agriculture, thereby improving food security and economic stability.

Study Configuration

• Spatial Scale: Regional to global, aiming for wide agricultural areas.
• Temporal Scale: Not explicitly defined, but implied to be continuous or seasonal for effective monitoring.

Methodology and Data

• Models used: Deep learning models (specific architectures not detailed in the provided text).
• Data sources: Geospatial technologies including Geographic Information Systems (GIS), Remote Sensing (RS), and Global Positioning Systems (GPS), implying data from satellite imagery, aerial photography, and ground-based positioning.

Main Results

The provided text is an introductory chapter and does not present specific results from a study. It outlines the importance and potential of the discussed methodologies.

Contributions

The article contributes by highlighting the transformative potential of integrating deep learning with geospatial technologies (GIS, remote sensing, GPS) for advanced crop health monitoring and efficient agricultural management, addressing the limitations of conventional, labor-intensive methods.

Funding

Not mentioned in the provided text.

Citation

@article{Singh2025Crop,
  author = {Singh, Yadvendra Pratap and Gangrade, Jayesh and Singh, Mukund Pratap and Solanki, Surendra and Chauhan, Shishir Singh},
  title = {Crop health monitoring using geospatial methods and deep learning},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-34113-7.00006-7},
  url = {https://doi.org/10.1016/b978-0-443-34113-7.00006-7}
}