Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Edge AI in Agriculture
- Overview of AI applications in farming.
- Advantages of Edge AI for real-time decision-making.
- Key challenges and limitations in smart agriculture.
AI-Driven Crop Monitoring
- Leveraging computer vision for plant health analysis.
- Detecting crop diseases using AI models.
- Implementing drone-based crop inspections.
Livestock Tracking and Behavior Analysis
- Edge AI for real-time livestock surveillance.
- Behavioral analytics and anomaly detection.
- Utilizing wearable sensors for precision livestock farming.
Automated Irrigation and Environmental Sensing
- AI-controlled irrigation systems.
- Monitoring soil moisture and climate conditions via IoT.
- Optimizing water usage through Edge AI.
Deploying Edge AI Models for Smart Farming
- Selecting appropriate AI frameworks and hardware.
- Comparing on-device processing with 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 Recommended Next Steps
Requirements
- Fundamental knowledge of AI and machine learning principles.
- Familiarity with IoT devices and sensor technologies.
- General awareness of agricultural practices and associated challenges.
Target Audience
- Agritech professionals.
- IoT specialists.
- AI engineers.
21 Hours
Testimonials (1)
That we can cover advance topic and work with real-life example
