📚 Table of Contents

• • Module 1: Foundations of Precision Agriculture
    • Introduction to Precision Farming and Smart Crop Monitoring
    • Data Sources in Agriculture: Satellite Imagery, IoT Sensors, Weather & Soil Data
    • Exploring NDVI and Remote Sensing Datasets
    • Hands-On: Visualizing Crop Health Data using Google Colab
    • Case Studies: Applications of Remote Sensing in Real Farms

• • Module 2: Deep Learning for Plant Disease Detection
    • Image-Based Disease Detection in Crops
    • Utilizing the PlantVillage Dataset & Understanding Common Crop Diseases
    • Developing CNN Models for Leaf Disease Classification
    • Model Training, Evaluation, and Prediction Techniques
    • Customizing Models for Specific Crops

• • Module 3: AI-Driven Crop Recommendation Systems
    • Overview of AI-Driven Decision Support in Agriculture
    • Leveraging Soil, Weather, and Historical Data for Crop Recommendations
    • Building a Crop Suggestion Tool using OpenAI or Custom Rules
    • Deploying Models via Streamlit or Google Colab
    • Use Cases: Crop Planning and Smart Irrigation Systems

• • Module 4: Deployment & Real-World Applications
    • Introduction to Model Deployment Platforms
    • Integrating AI Models with IoT Devices
    • Real-Time Data Processing and Visualization
    • Ensuring Scalability and Maintenance
    • Case Studies: Global Success Stories in AI-Powered Farming

• Module 5: Future Trends and Industry Opportunities
  • Emerging Technologies in Smart Agriculture
  • Sustainability and Environmental Impact
  • Career Paths: Precision Agriculture Analyst, AgriTech Data Scientist, and More
  • Interactive Q&A and Capstone Project Brief
  • Preparing for the Future of Digital Farming