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URN
C22.0.297
Themes
AI , Data & Insights , Autonomous Operations , Beyond Connectivity

What is the main problem you have identified:

Smart Agriculture is highly dependent on IoT integrations and autonomous robots. In addition, advanced data analytics and AI/ML capabilities are needed to provide near real-time insight into the field data. The dependency on various technology stacks with diverse QoS requirements poses a challenge on the services providers.

This catalyst tries to address a few of these challenges with the implementation of smart agriculture use cases and to provide a blueprint for scalability, and optimization.

How will this project team solve the challenge or problem:

We expect that smart agriculture pain points can be solved using the following technologies (but not limited to):
• Network Slicing
• MEC Orchestration
• Edge AI/ML application framework

How will the success of the solution be measured:

The following use cases will need to be demonstrated:
a. An integrated solution to meet the demand of smart agriculture leveraging on the 5G technologies like network slicing, MEC, AI to meet the latency and data throughput requirements.
b. A fully functional MEC framework addressing challenges like Heterogeneity in the deployment environment, life cycle management, future proof design, vertical industry requirements, and zero-touch network.
c. A strategy for seamless deployment of AI/ML application and POC for Smart Agriculture IoT and robotics integration.

The following outcomes are expected from this catalyst:
a. Integrated solution architecture for smart agriculture
b. A MEC framework for orchestration and automation
c. A strategy paper for seamless deployment of AI/ML application at the edge
d. A POC for Smart Agriculture IoT and robotics integration.