📚 Syllabus & Course Curriculum
Agriculture & Food Technology
Module-by-module breakdown of Digital Twin Agriculture: Mastering Multi-Source Data Fusion & Machine Learning, from foundations to a certified capstone project.
Outline
Explore crop intelligence and precision agriculture concepts • Process satellite imagery and NDVI indices using Python (NumPy, Pandas, Rasterio) • Detect stress signals and visualize health metrics
Outline
Clean and engineer features from historic MDPI crop datasets • Build supervised models (Random Forest, XGBoost, LSTM) for yield prediction • Integrate climate and environmental variables and evaluate model performance
Outline
Design AI‑driven irrigation, fertilization, and pest‑management strategies • Create visual dashboards with Matplotlib, Seaborn, and Plotly • Analyze trends and generate actionable agronomic recommendations
Earn government-registered certification in Digital Twin Agriculture: Mastering Multi-Source Data Fusion & Machine Learning
e-Certificate and e-Marksheet issued on successful completion.
