About Workshop
Master AI-driven, climate-smart agriculture in this 3-day hands-on workshop. Learn to clean and analyze agricultural and climate datasets, engineer features, build predictive models for crop yield and climate risk, and leverage satellite data for crop-health insights. Complete a mini project workflow integrating AI, climate, and geospatial data for actionable, sustainable agriculture solutions.
Aim
To empower participants with practical AI and data skills for climate-smart agriculture, enabling predictive modeling, crop-health monitoring, and actionable insights for sustainable farming.
What Participants Will Learn
- Prepare and clean agricultural and climate datasets for AI applications
- Engineer meaningful features from crop, soil, and climate data
- Build and evaluate machine learning models for crop yield and climate-risk prediction
- Analyze satellite imagery and NDVI for crop-health and vegetation monitoring
- Integrate agricultural, climate, and geospatial data into actionable AI workflows
- Develop a mini project demonstrating practical, sustainable agriculture solutions
Structure
📅 Day 1: Agricultural Data Preparation and Climate-Smart Feature Engineering
- Understanding agricultural datasets: crop type, yield, region, rainfall, temperature, humidity, and soil indicators
- Data cleaning: handling missing values, outliers, inconsistent units, and duplicate records
- Preparing climate and crop variables for machine learning models
- Feature engineering from rainfall, temperature, soil condition, crop type, and seasonal patterns
- Exploratory data analysis for identifying crop productivity and climate trends
- Creating machine-learning-ready datasets for crop yield and climate-risk prediction
🛠️ Hands-on:
- Clean datasets, create features, and visualize crop-climate patterns
📅 Day 2: AI Model Development for Crop Yield Prediction and Climate-Risk Analysis
- Machine learning workflow for climate-smart agriculture
- Regression models for crop yield prediction
- Classification models for crop suitability and climate-risk analysis
- Feature selection for identifying key crop, soil, and climate variables
- Model training and testing using agriculture-climate datasets
- Model evaluation: RMSE, MAE, R², accuracy, precision, recall, F1-score
- Interpreting model results using feature importance and explainability techniques
🛠️ Hands-on:
- Train models, evaluate performance, and interpret key factors affecting crop yield and risk
📅 Day 3: Remote Sensing, Vegetation Monitoring, and Climate-Smart Mini Project
- Remote sensing workflow for crop and vegetation monitoring
- Satellite data interpretation using Sentinel-2 or Landsat imagery
- NDVI-based crop-health and vegetation-stress analysis
- Geospatial visualization for agricultural decision-making
- Integrating crop, climate, and satellite-derived data into AI workflows
- Preparing a technical mini project for climate-smart agriculture
- Result visualization and technical reporting for research or professional use
🛠️ Hands-on:
- NDVI calculation, crop-health mapping, and mini project workflow development
Important Dates
Registration Ends
4: 30 PM IST
Workshop Dates
2026-06-16
5: 30 PM IST
5: 30 PM IST
What You Will Gain
Outcomes
- Create AI-ready agricultural and climate datasets
- Develop predictive models for crop yield and climate-risk assessment
- Analyze satellite and geospatial data for crop-health monitoring
- Perform feature engineering and interpret model results
- Integrate AI, climate, and remote sensing data into actionable workflows
- Deliver a mini project showcasing climate-smart agriculture solutions
Who Should Attend
- Researchers, PhD scholars, and academicians in agriculture, environmental science, or data science
- Professionals working in agri-tech, climate research, sustainability, or precision farming
- Students with foundational knowledge in Python and basic data analysis
- Individuals interested in applying AI and remote sensing for sustainable agriculture