Zero-Knowledge Agricultural Intelligence: Combining TinyML, Federated Learning, and Blockchain for Future Food Systems
Main article
Abstract
The convergence of resource-constrained machine learning, privacy-preserving distributed computation, and decentralized governance technologies offers a transformative pathway for automating food safety compliance monitoring across heterogeneous and geographically dispersed agricultural settings. This paper introduces a unified framework that integrates TinyML-based edge inference, clustered federated learning with graph-attention mechanisms, and blockchain smart contracts secured by Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs). Deployed on low-power microcontrollers, the edge intelligence layer performs real-time classification of livestock health, hygiene indicators, and milk quality from multimodal sensor streams without transmitting raw data off-farm. Federated learning with density-based outlier filtering enables collaborative model improvement across heterogeneous farm environments while maintaining data locality. Compliance inferences are cryptographically committed through ZK-SNARK circuits and verified on a Layer-2 Polygon zkEVM blockchain, where smart contracts automate license management, audit triggering, and reputation scoring. Experimental evaluation demonstrates 88.1% on-device inference accuracy at 33.8 ms latency with 64.6 KB RAM usage, a 97.7% reduction in communication payload versus standard TFLite Micro implementations, and sustained accuracy above 90% under 20% sensor noise injection. The framework provides a scalable, privacy-preserving, and legally defensible instrument for automated regulatory compliance applicable to smallholder agricultural operations.
