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Duration 21 hours
Course Outline
Introduction to TinyML in Agriculture
- Grasping the capabilities of TinyML
- Key agricultural use cases
- Benefits and constraints of on-device intelligence
Hardware and Sensor Ecosystems
- Microcontrollers suited for edge AI
- Common agricultural sensors
- Considerations for energy and connectivity
Data Collection and Preprocessing
- Methods for acquiring field data
- Cleaning sensor and environmental data
- Feature extraction for edge models
Developing TinyML Models
- Selecting models for constrained devices
- Training workflows and validation processes
- Optimising model size and efficiency
Deploying Models to Edge Devices
- Utilising TensorFlow Lite for microcontrollers
- Flashing and executing models on hardware
- Resolving deployment challenges
Smart Agriculture Applications
- Assessing crop health
- Detecting pests and diseases
- Managing precision irrigation
IoT Integration and Automation
- Connecting edge AI to farm management platforms
- Implementing event-driven automation
- Real-time monitoring workflows
Advanced Optimisation Techniques
- Quantisation and pruning strategies
- Approaches to battery optimisation
- Scalable architectures for large-scale deployments
Summary and Next Steps
Requirements
- Knowledge of IoT development workflows
- Experience handling sensor data
- General understanding of embedded AI concepts
Target Audience
- Agritech engineers
- IoT developers
- AI researchers
