- Level: Beginner
- Prerequisites: None
- Assessments: Weekly Micro Assessments; Full Assessment in the Final Week
Week 1: Basics of AI in Agriculture
Learning Outcome:Understand how AI is used in farming.1.1 Challenges in agriculture.
1.2 Overview of AI applications in agriculture.
1.3 Introduction to crop yield prediction.
Practical Component
Explore agricultural datasets.
Week 2: Data Preparation for Yield Prediction
Learning Outcome: Process and prepare agricultural data.1.4 Sources of agricultural data (weather, soil, satellite).
1.5 Feature engineering for crop yield models.
1.6 Handling missing and noisy data.
Practical Component
Clean and prepare a crop yield dataset.
Week 3: Building Predictive Models
Learning Outcome: Build AI models to predict crop yields.1.7 Regression models for yield prediction.
1.8 Combining weather and soil data.
1.9 Testing model performance.
Practical Component
Build a crop yield prediction model.
Week 4: Advanced Techniques for Agriculture AI
Learning Outcome: Explore advanced AI techniques for precision farming.1.10 Using satellite data for predictions.
1.11 Computer vision for detecting crop health.
1.12 Integrating IoT sensors with AI models.
Practical Component
Build a crop health detection model using computer vision.
Week 5: Real-World Challenges
Learning Outcome: Understand challenges in applying AI to agriculture.1.13 Data limitations in farming regions.
1.14 Ethical and environmental considerations.
1.15 Case studies of successful AI applications.
Practical Component
Solve a real-world agricultural AI challenge.
Week 6: Final Project
Learning Outcome: Build and demonstrate a crop prediction system.1.16 Define a use case (e.g., wheat yield prediction).
1.17 Build and test the system.
1.18 Present the solution and its potential impact.
Practical Component
Demonstrate your AI-powered agricultural solution.