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Duration 21 hours
Course Outline
Introduction to TinyML in Agriculture
- Exploring the capabilities of TinyML
- Primary agricultural applications
- Benefits and limitations of on-device intelligence
Hardware and Sensor Infrastructure
- Microcontrollers suitable for edge AI
- Standard sensors used in agriculture
- Considerations for energy efficiency and connectivity
Data Gathering and Preparation
- Methods for acquiring field data
- Processing and cleaning sensor and environmental inputs
- Extracting features for edge-based models
Developing TinyML Models
- Selecting models suited for constrained devices
- Establishing training and validation processes
- Refining model size and performance efficiency
Implementing Models on Edge Hardware
- Utilizing TensorFlow Lite for microcontrollers
- Deploying and executing models on physical hardware
- Resolving common deployment challenges
Applications in Smart Agriculture
- Evaluating crop health
- Identifying pests and diseases
- Controlling precision irrigation systems
IoT Integration and Automation
- Linking edge AI with farm management systems
- Implementing event-driven automation
- Designing real-time monitoring workflows
Advanced Optimization Strategies
- Techniques for quantization and pruning
- Methods for optimizing battery life
- Designing scalable architectures for large-scale deployments
Conclusion and Future Directions
Requirements
- Working knowledge of IoT development processes
- Practical experience handling sensor data
- Foundational grasp of embedded AI principles
Target Audience
- Agri-tech engineers
- IoT developers
- AI researchers
