AI-Driven Climate-Smart Agriculture and Sustainable Food Systems

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.

• 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

📅 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

🧰 Tools Covered: Python, Colab / Jupyter, Pandas, NumPy, Matplotlib, Seaborn

📅 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

🧰 Tools Covered: Python, Colab / Jupyter, Scikit-learn, Random Forest, XGBoost, SHAP, Matplotlib, Seaborn

📅 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

🧰 Tools Covered: Google Earth Engine, Python, Colab, GeoPandas, Rasterio, Folium, Plotly, Optional: Power BI / Looker Studio

• 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

• 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