Course Outline

Introduction to Edge AI in Agriculture

  • Overview of AI applications in farming
  • The benefits of Edge AI for real-time decision-making
  • Key challenges and limitations in smart agriculture

AI-Powered Crop Monitoring

  • Using computer vision for plant health analysis
  • Identifying crop diseases with AI models
  • Implementing drone-based crop inspections

Livestock Tracking and Behavior Analysis

  • Edge AI for real-time livestock monitoring
  • Behavioral analytics and anomaly detection
  • Wearable sensors for precision livestock farming

Automated Irrigation and Environmental Sensing

  • AI-driven irrigation control systems
  • Soil moisture and climate monitoring with IoT
  • Optimizing water usage with Edge AI

Deploying Edge AI Models for Smart Farming

  • Choosing the right AI frameworks and hardware
  • On-device processing vs. cloud-based solutions
  • Ensuring scalability and efficiency in Edge AI systems

Future Trends and Challenges in Agri-AI

  • Ethical considerations in AI-driven agriculture
  • Emerging innovations in agritech and Edge AI
  • Regulatory compliance and data security concerns

Summary and Next Steps

Requirements

  • Basic understanding of AI and machine learning concepts
  • Familiarity with IoT devices and sensor technologies
  • General knowledge of agricultural practices and challenges

Audience

  • Agritech professionals
  • IoT specialists
  • AI engineers
 21 Hours

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