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Duration 21 hours
Course Outline
Introduction to TinyML in Agriculture
- Overview of TinyML potential
- Primary applications in agriculture
- Advantages and limitations of on-device intelligence
Hardware and Sensor Ecosystem
- Microcontrollers for edge AI
- Typical agricultural sensors
- Considerations for energy and connectivity
Data Collection and Preprocessing
- Methods for acquiring field data
- Processing sensor and environmental data
- Extracting features for edge models
Building TinyML Models
- Selecting models for resource-constrained devices
- Training processes and verification
- Enhancing model compactness and efficiency
Deploying Models to Edge Devices
- Utilizing TensorFlow Lite for microcontrollers
- Loading and executing models on hardware
- Addressing deployment challenges
Smart Agriculture Applications
- Evaluating crop health
- Identifying pests and diseases
- Managing precision irrigation
IoT Integration and Automation
- Linking edge AI with farm management systems
- Automation based on events
- Workflows for real-time monitoring
Advanced Optimization Techniques
- Strategies for quantization and pruning
- Techniques for optimizing battery life
- Scalable designs for extensive deployments
Overview and Future Directions
Requirements
- Knowledge of IoT development processes
- Experience handling sensor data
- Basic understanding of embedded AI principles
Target Audience
- Agritech engineers
- IoT developers
- AI researchers
