About Workshop
This 3-day mentor-based workshop introduces participants to the integration of satellite imagery, NDVI, ERA5 climate data and machine-learning techniques for agricultural monitoring and forecasting.
Participants will learn how to analyze crop health, detect climate-related stress, integrate environmental datasets and build AI models for crop-condition and yield prediction using practical tools and real-world workflows.
Aim
To equip participants with practical skills in remote sensing, climate-data analysis and AI-based crop forecasting for climate-smart precision agriculture.
What Participants Will Learn
- Understand the fundamentals of climate-smart precision agriculture.
- Explore satellite datasets for crop monitoring.
- Calculate and interpret NDVI and related vegetation indices.
- Access and process ERA5-Land and other climate datasets.
- Integrate climate, weather and remote-sensing data.
- Identify drought, heat and vegetation-stress patterns.
- Build machine-learning models for crop forecasting.
- Apply time-series forecasting to agricultural data.
- Evaluate model performance using standard metrics.
- Use explainable AI to interpret climate and crop relationships.
- Convert analytical results into data-driven agricultural decisions.
Structure
Day 1: Remote Sensing & Vegetation Monitoring
- Climate-smart precision agriculture fundamentals
- Satellite imagery and spectral bands
- Sentinel-2, Landsat and MODIS
- NDVI, EVI, NDWI and SAVI
- Crop-health and vegetation-stress assessment
- Agricultural mapping with Google Earth Engine
- Introduction to ERA5-Land, NASA POWER and CHIRPS
Tools: Google Earth Engine, Sentinel-2, Landsat, MODIS, Python, Google Colab, geemap Hands-on: Create and interpret an NDVI-based crop-health map.
Day 2: Climate Data Integration & Crop Stress Analysis
- ERA5/ERA5-Land climate-data extraction
- Temperature, rainfall, humidity and soil-moisture analysis
- Climate-data cleaning and aggregation
- Integration of NDVI and climate datasets
- Drought, heat-stress and climate-anomaly detection
- Correlation and feature analysis
- Dataset preparation for machine learning
Tools: ERA5-Land, NASA POWER, CHIRPS, Google Earth Engine, Python, Pandas, NumPy, Matplotlib Hands-on: Integrate ERA5 + NDVI data and identify climate-driven crop stress.
Day 3: AI-Based Crop Forecasting & Decision Support
- AI and machine learning in agriculture
- Feature engineering and predictor selection
- Random Forest and XGBoost
- Time-series forecasting with Prophet
- Crop-condition and yield prediction
- Model evaluation using MAE, RMSE and R²
- Explainable AI using SHAP
- Climate-smart decision support
Important Dates
Registration Ends
4:30 PM
Workshop Dates
2026-09-07
5:00 PM
5:00 PM
What You Will Gain
Outcomes
- Generate and interpret satellite-based crop-health maps.
- Extract and analyze climate variables relevant to agriculture.
- Combine NDVI, weather and climate datasets.
- Detect climate-driven crop-stress patterns.
- Prepare agricultural datasets for AI modelling.
- Develop crop-condition and yield-prediction models.
- Apply XGBoost, Random Forest and Prophet for agricultural forecasting.
- Evaluate predictions using MAE, RMSE and R².
- Interpret model outputs using SHAP and feature importance.
- Build an end-to-end AI-driven precision-agriculture workflow.
Who Should Attend
- Undergraduate and postgraduate students
- PhD scholars and research scholars
- Faculty members and academicians
- Agricultural and environmental researchers
- Agronomy and crop-science professionals
- Remote-sensing and GIS professionals
- Data-science and AI enthusiasts
- Agri-tech and precision-agriculture professionals
- Climate and sustainability researchers
- Industry professionals working in agriculture, geospatial analytics or environmental monitoring