Rana et al. (2025) Integrated Artificial Intelligence in Weather Forecasting for Agriculture: Opportunities, Challenges, and the Road Ahead

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

Identification

• Journal: International Journal of Environment and Climate Change
• Year: 2025
• Date: 2025-11-22
• Authors: A. Rana, Asma Fayaz Lone
• DOI: 10.9734/ijecc/2025/v15i115137

Research Groups

Not explicitly stated in the provided text, but typically involves researchers in agricultural science, artificial intelligence, meteorology, and computer science.

Short Summary

This review evaluates the application of Artificial Intelligence (AI) in agricultural weather forecasting, finding that AI, particularly hybrid AI-NWP models and multimodal data fusion, significantly improves prediction accuracy, reduces errors, and shortens lead times for various weather parameters and extreme events.

Objective

• To evaluate the use of AI, including machine learning and deep learning, in predicting rainfall, temperature, humidity, wind, and extreme events for agricultural weather forecasting, assessing its impact on prediction accuracy, error reduction, and lead time.

Study Configuration

• Spatial Scale: Microclimate to regional (case studies within and beyond India), supporting field-level decision making.
• Temporal Scale: Short-term to medium-term forecasting, enabling real-time advisories and early-warning systems.

Methodology and Data

• Models used: Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and hybrid AI-Numerical Weather Prediction (NWP) models.
• Data sources: Satellite data, on-ground sensors, and multimodal data fusion.

Main Results

• AI applications in agricultural weather forecasting demonstrate increased prediction accuracy, reduction in prediction error, and shorter lead times for rainfall, temperature, humidity, wind, and extreme events.
• Key advancements include hybrid AI-NWP models, multimodal data fusion, and IoT-based sensor networks.
• These advancements provide real-life benefits for irrigation scheduling, pest and disease management, and disaster early-warning systems.
• Hyperlocal advisory platforms and edge computing support real-time, field-level decision making for precision farming.
• Challenges remain, including low data quality, high computational needs, poor rural infrastructure, and socio-economic restrictions.
• Emerging pathways like adaptive AI, blockchain-secured edge systems, and customized advisories offer significant opportunities for resilience, resource-use efficiency, and food security.

Contributions

• Provides a comprehensive review of the current state and impact of AI in agricultural weather forecasting.
• Identifies major progress areas (hybrid AI-NWP, multimodal data fusion, IoT) and their practical applications in agriculture.
• Highlights existing challenges hindering AI adoption and proposes future pathways for research and policy.
• Emphasizes the potential of AI to enhance resilience, resource efficiency, and food security amidst climate variability.

Funding

Not specified in the provided text.

Citation

@article{Rana2025Integrated,
  author = {Rana, A. and Lone, Asma Fayaz},
  title = {Integrated Artificial Intelligence in Weather Forecasting for Agriculture: Opportunities, Challenges, and the Road Ahead},
  journal = {International Journal of Environment and Climate Change},
  year = {2025},
  doi = {10.9734/ijecc/2025/v15i115137},
  url = {https://doi.org/10.9734/ijecc/2025/v15i115137}
}