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
- Understanding the capabilities of TinyML
- Key use cases in agriculture
- Constraints and benefits of on-device intelligence
Hardware and Sensor Ecosystem
- Microcontrollers for edge AI
- Commonly used 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
- Optimizing model size and efficiency
Deploying Models to Edge Devices
- Utilizing TensorFlow Lite for microcontrollers
- Flashing and executing models on hardware
- Troubleshooting deployment issues
Applications in Smart Agriculture
- 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
- Strategies for quantization and pruning
- Approaches for battery optimization
- Scalable architectures for large-scale deployments
Summary and Next Steps
Requirements
- Familiarity with IoT development workflows
- Experience working with sensor data
- A general understanding of embedded AI concepts
Target Audience
- Agritech engineers
- IoT developers
- AI researchers
