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Duration 21 hours
Course Outline
Introduction to TinyML in Agriculture
- Exploring the capabilities of TinyML
- Key use cases in agriculture
- Constraints and advantages of on-device intelligence
Hardware and Sensor Ecosystem
- Microcontrollers for edge AI applications
- Essential agricultural sensors
- Energy and connectivity requirements
Data Collection and Preprocessing
- Methods for field data acquisition
- Cleaning sensor and environmental data
- Feature extraction for edge models
Building TinyML Models
- Selecting models for constrained devices
- Training workflows and validation processes
- Optimizing model size and efficiency
Deploying Models to Edge Devices
- Utilizing TensorFlow Lite for microcontrollers
- Flashing and executing models on hardware
- Resolving deployment challenges
Smart Agriculture Applications
- Assessing crop health
- Detecting pests and diseases
- Controlling precision irrigation
IoT Integration and Automation
- Linking edge AI to farm management platforms
- Implementing event-driven automation
- Designing real-time monitoring workflows
Advanced Optimization Techniques
- Quantization and pruning strategies
- Approaches to battery optimization
- Scalable architectures for large-scale deployments
Summary and Next Steps
Requirements
- Proficiency with IoT development workflows
- Hands-on experience handling sensor data
- A solid grasp of embedded AI concepts
Target Audience
- Agritech engineers
- IoT developers
- AI researchers
