Deshpande et al. (2025) Remote Sensing and Precision Agronomy: A Comprehensive Review of Applications and Prospects

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Advances in Biology & Biotechnology
• Year: 2025
• Date: 2025-11-05
• Authors: Harish Deshpande, Harshada Deshmukh, Ningaraj Dalawai
• DOI: 10.9734/jabb/2025/v28i113259

Research Groups

This is a review paper synthesizing existing literature; therefore, the "research group" refers to the authors who conducted the synthesis. Specific affiliations are not provided in the text.

Short Summary

This review synthesizes the foundations, platforms, and analytical methods of remote sensing, connecting them to core agronomic decisions. It concludes that remote sensing is mature for many tasks and rapidly improving, enabling more precise, profitable, and sustainable agronomy when integrated into validated workflows.

Objective

• To synthesize the foundations, platforms, and analytical methods of remote sensing and connect them to core agronomic decisions, including soil and crop characterisation, irrigation scheduling, nutrient management, weed and disease control, and yield forecasting.

Study Configuration

• Spatial Scale: Field to regional/global scales.
• Temporal Scale: From periodic scouting to continuous, quantitative monitoring.

Methodology and Data

• Models used: Physics-based energy balance methods for evapotranspiration, crop system models (e.g., DSSAT, APSIM).
• Data sources: Satellite constellations, synthetic aperture radar (SAR), thermal sensors, low-altitude drones, established indices (e.g., NDVI, EVI, SAVI), hyperspectral approaches, deep learning approaches, multisensor data fusion, operational products (e.g., OpenET, GEOGLAM Crop Monitor).

Main Results

• Remote sensing has transformed field observation from periodic scouting to continuous, quantitative monitoring.
• Established indices, physics-based energy balance methods, SAR for all-weather crop mapping, and emerging hyperspectral and deep learning approaches are key tools.
• Multisensor data fusion and model-data integration with crop system models are crucial for advanced applications.
• Operational products like OpenET and GEOGLAM Crop Monitor demonstrate practical implementation.
• Key barriers include interoperability, calibration, and data governance, while implementation pathways and economics are also discussed.
• Near-term prospects include UAV satellite fusion, field-scale evapotranspiration, edge AI, and standards for interoperable farm data.
• Long-term needs involve privacy, equitable access, and robust decision support.
• Remote sensing is mature for many tasks and rapidly improving for others, enabling more precise, profitable, and sustainable agronomy when integrated into repeatable, validated workflows, provided robust calibration/validation, transparent data contracts, and farmer-centric design are in place.

Contributions

• Provides a comprehensive synthesis of the current state and future prospects of remote sensing in precision agronomy.
• Connects diverse remote sensing technologies and analytical methods to specific agronomic decision-making processes.
• Summarizes both established techniques and emerging innovations (e.g., hyperspectral, deep learning, multisensor fusion).
• Identifies critical implementation barriers, economic considerations, and outlines pathways for adoption.
• Highlights near-term opportunities and long-term challenges (e.g., data privacy, equitable access) for the field.

Funding

Not specified in the provided text.

Citation

@article{Deshpande2025Remote,
  author = {Deshpande, Harish and Deshmukh, Harshada and Dalawai, Ningaraj},
  title = {Remote Sensing and Precision Agronomy: A Comprehensive Review of Applications and Prospects},
  journal = {Journal of Advances in Biology & Biotechnology},
  year = {2025},
  doi = {10.9734/jabb/2025/v28i113259},
  url = {https://doi.org/10.9734/jabb/2025/v28i113259}
}