Edge AI for Agriculture: Smart Farming and Precision Monitoring

• 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

Edge AI is transforming modern agriculture by enabling real-time, AI-powered decision-making for crop monitoring, livestock tracking, and automated irrigation.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy Edge AI solutions for smart farming.

By the end of this training, participants will be able to:

• Understand the role of Edge AI in precision agriculture.
• Implement AI-driven crop and livestock monitoring systems.
• Develop automated irrigation and environmental sensing solutions.
• Optimize agricultural efficiency using real-time Edge AI analytics.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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