📅 Day 1: Climate–Crop Data Foundations & Agroclimatic Intelligence
- Introduction to Generative AI in climate and agricultural research
- Understanding weather–crop interactions and yield variability
- Overview of climate, crop, soil, and yield data sources
- Data preprocessing, seasonal alignment, and missing-value handling
- Development of rainfall anomalies, heat-stress days, and Growing Degree Days
🛠️ Hands-on
- Retrieve historical weather data using the NASA POWER API
- Integrate sample weather and crop-yield datasets
- Visualize temperature, rainfall, and crop-yield relationships
Hands-on Tools: Google Colab, Python, pandas, NumPy,
Matplotlib, NASA POWER, and FAOSTAT
📅 Day 2: Generative AI for Synthetic Weather Generation
- Importance of synthetic weather data in agricultural forecasting
- Introduction to VAE, GAN, TimeGAN, and diffusion-based weather models
- Preparing multivariate climate time-series data for generative modelling
- Generating normal, drought, heatwave, and excess-rainfall scenarios
- Validating synthetic weather using statistical and temporal indicators
🛠️ Hands-on
- Prepare temperature and rainfall sequences for AI modelling
- Generate synthetic seasonal weather using a lightweight Conditional VAE
- Compare real and synthetic weather distributions and correlations
Hands-on Tools: Google Colab, Python, TensorFlow/Keras,
scikit-learn, SciPy, and pandas
📅 Day 3: Long-Range Yield Prediction & Explainable Climate-Risk Modelling
- Fundamentals of seasonal and long-range crop-yield prediction
- Application of Random Forest, XGBoost, LSTM, and Transformer models
- Integration of observed and synthetic weather with crop-yield data
- Explainable AI for identifying key weather and crop-growth factors
- Yield uncertainty, drought risk, heat stress, and climate-smart decision-making
🛠️ Hands-on
- Train an XGBoost-based crop-yield prediction model
- Interpret climate-variable effects using SHAP analysis
- Generate expected, adverse, and optimistic yield scenarios
Hands-on Tools: Google Colab, Python, XGBoost, SHAP,
scikit-learn, pandas, and Matplotlib
