Course Outline

Introduction to TinyML in Agriculture

  • Understanding the capabilities of TinyML
  • Key agricultural use cases for government applications
  • Constraints and benefits of on-device intelligence for government operations

Hardware and Sensor Ecosystem

  • Microcontrollers for edge AI in agricultural settings
  • Common agricultural sensors used for government projects
  • Energy and connectivity considerations for government deployments

Data Collection and Preprocessing

  • Methods for field data acquisition in government contexts
  • Cleaning sensor and environmental data for government use
  • Feature extraction techniques for edge models in government applications

Building TinyML Models

  • Selecting appropriate models for constrained devices in government settings
  • Training workflows and validation processes for government projects
  • Optimizing model size and efficiency for government deployments

Deploying Models to Edge Devices

  • Utilizing TensorFlow Lite for microcontrollers in government applications
  • Flashing and running models on hardware for government use
  • Troubleshooting deployment issues for government projects

Smart Agriculture Applications

  • Crop health assessment for government initiatives
  • Pest and disease detection in government-managed farms
  • Precision irrigation control for government agriculture programs

IoT Integration and Automation

  • Connecting edge AI to farm management platforms for government operations
  • Implementing event-driven automation in government projects
  • Real-time monitoring workflows for government agriculture initiatives

Advanced Optimization Techniques

  • Quantization and pruning strategies for government applications
  • Battery optimization approaches for government devices
  • Scalable architectures for large-scale government deployments

Summary and Next Steps

Requirements

  • Familiarity with Internet of Things (IoT) development workflows for government and private sector applications.
  • Experience working with sensor data in various environmental and operational contexts.
  • A general understanding of embedded artificial intelligence concepts and their practical applications.

Audience

  • Agritech engineers involved in smart agriculture initiatives for government projects.
  • IoT developers supporting public sector innovation and technology integration.
  • AI researchers focusing on advanced technologies for government and industry use.
 21 Hours

Number of participants


Price per participant

Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.

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