📅 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
