AI-Driven Climate-Smart Precision Agriculture: Remote Sensing, ERA5, NDVI & Crop Forecasting

• 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.

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

• 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.

• 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