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.
Duration 21 hours
Course Outline
Introduction to TinyML in Agriculture
- Exploring TinyML capabilities
- Key agricultural use cases
- Constraints and advantages of on-device intelligence
Hardware and Sensor Ecosystem
- Microcontrollers suited for edge AI
- Commonly used agricultural sensors
- Considerations for energy efficiency and connectivity
Data Collection and Preprocessing
- Methods for acquiring field data
- Cleaning sensor and environmental datasets
- Extracting features suitable for edge models
Building TinyML Models
- Selecting models appropriate for constrained devices
- Training workflows and validation processes
- Optimizing model size and computational efficiency
Deploying Models to Edge Devices
- Utilizing TensorFlow Lite for microcontrollers
- Flashing and executing models on hardware
- Troubleshooting common deployment issues
Smart Agriculture Applications
- Assessing crop health
- Detecting pests and diseases
- Controlling precision irrigation
IoT Integration and Automation
- Connecting edge AI to farm management platforms
- Implementing event-driven automation
- Establishing real-time monitoring workflows
Advanced Optimization Techniques
- Applying quantization and pruning strategies
- Approaches for battery optimization
- Designing scalable architectures for large-scale deployments
Summary and Next Steps
Requirements
- Proficiency with IoT development workflows
- Practical experience working with sensor data
- Foundational understanding of embedded AI concepts
Audience
- Agri-tech engineers
- IoT developers
- AI researchers
