Generative AI for Synthetic Weather Generation and Long-Range Yield Prediction

The workshop aims to equip participants with practical skills in Generative AI, synthetic weather modelling, and long-range crop-yield forecasting. It focuses on enabling participants to generate realistic climate scenarios, assess weather-related agricultural risks, and develop reliable predictive models that support climate-resilient farming, strategic crop planning, and evidence-based agricultural decision-making.

• Understand the role of Generative AI in weather and agricultural modelling.
• Prepare and integrate climate, soil, crop, and yield datasets.
• Generate realistic synthetic weather scenarios using AI models.
• Develop machine-learning models for long-range yield prediction.
• Evaluate model accuracy, uncertainty, and climate-related crop risks.
• Visualize and interpret forecasts for agricultural decision-making.

📅 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

• Researchers and PhD scholars
• Academicians and faculty members
• Agricultural and climate scientists
• Data science and AI professionals
• Agritech and crop-modelling professionals
• Remote sensing and GIS researchers
• Professionals in food security, crop insurance, and climate-risk assessment