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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
- Utilizing 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
- Selecting the appropriate AI frameworks and hardware.
- On-device processing versus 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
- Fundamental understanding of AI and machine learning concepts.
- Familiarity with IoT devices and sensor technologies.
- General knowledge of agricultural practices and associated challenges.
Audience
- Agritech professionals.
- IoT specialists.
- AI engineers.
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example