About Workshop
This workshop introduces participants to the emerging role of Generative AI in agricultural forecasting by combining synthetic weather generation with long-range crop-yield prediction. Participants will explore how AI can simulate realistic future weather conditions, analyse climate variability, and integrate weather, soil, crop, and historical yield data to estimate agricultural productivity.
Through practical, data-driven workflows, the workshop will demonstrate how advanced AI models can support early risk identification, climate-impact assessment, seasonal crop planning, and resilient agricultural decision-making. It is designed to help researchers and professionals translate complex environmental data into meaningful forecasts for sustainable and climate-smart agriculture.
Aim
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.
What Participants Will Learn
- 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.
Structure
📅 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
Important Dates
Registration Ends
4: 30 PM IST
Workshop Dates
2026-08-24
5 :30 PM IST
5 :30 PM IST
What You Will Gain
Outcomes
- Create synthetic weather datasets for agricultural research.
- Build and evaluate crop-yield prediction models.
- Analyse the influence of temperature, rainfall, soil, and climate variability on yield.
- Estimate future crop performance under different weather scenarios.
- Interpret prediction uncertainty and identify production risks.
- Apply AI-based forecasts to climate-smart agriculture and crop-planning decisions.
Who Should Attend
- 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