Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management

TL;DR

This paper proposes an integrated 6G-enabled UAV, AI, and blockchain architecture for agricultural supply-chain management, emphasizing on-device AI with resource-aware model selection to improve traceability and robustness.

Contribution

It introduces a novel on-device AI framework with iterative pruning for UAVs, enabling resource-accuracy trade-offs and adaptive model updates in dynamic 6G networks.

Findings

Multiple resource-specific models improve UAV performance.

Iterative pruning enhances model deployment flexibility.

Adaptive model selection mitigates UAV flight failures.

Abstract

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality-of-service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI and blockchain for agricultural supply-chain management with the purpose of ensuring traceability, transparency, tracking inventories and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAV, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.