Units
1. Introduction to Artificial Intelligence and Verification Discipline in Agricultural Engineering 2. Precision Agriculture and Structuring of Agricultural Data 3. Plant Health Monitoring with Satellite and Drone: NDVI and Vegetation Indices 4. Soil Analysis Interpretation and Fertilization Optimization 5. Irrigation Optimization and Water Management 6. Disease and Pest Diagnosis: Image Analysis and Decision 7. Agricultural Decision Support with Weather and Climate Data 8. Yield Estimation and Agricultural Modeling 9. Agricultural Data Analysis with Python 10. Traceability, Agricultural Registration and Supply Chain 11. Ethics, Privacy, Boundaries and End-to-End Integration
Unit 9 / 11

Agricultural Data Analysis with Python

Gains:

  • Ability to generate and run code for loading, cleaning and summarizing field/sensor data using pandas with AI
  • Ability to visualize analyzes such as NDVI time series and fertilizer/yield relationship with graphs
  • Ability to test AI-generated code with small data and detect unit, index formula and logic errors
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