Green Dairy Compliance Innovation through TinyML, Federated Learning, and Smart Contract Governance
Main article
Abstract
The global dairy industry confronts a persistent structural challenge in operationalising food safety and animal welfare compliance. Manual inspection regimes and intermittent audits are demonstrably inadequate for the heterogeneous, geographically dispersed landscape of small-scale farming, where data integrity, real-time monitoring capability, and regulatory transparency are simultaneously compromised. This article presents GreenDairyChain, an integrated compliance innovation framework that synthesises four enabling technologies: GreenEdgeML (a lightweight TinyML inference engine optimised for microcontroller-class devices), Privacy-Preserving Federated Learning (FL) with Graph Attention Network (GAT)-based dynamic clustering, Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) for cryptographic compliance verification, and a Layer-2 Polygon zkEVM Blockchain with domain-specific smart contracts governing farm identity, violation detection, audit triggers, and licence management. GreenEdgeML executes multimodal sensor fusion across four signal modalities (body temperature, accelerometer activity, ammonia concentration, and milk pH) entirely on-device using 8-bit integer quantisation, consuming 64.6 KB RAM and 82.7 mW per inference cycle on the ESP32 platform. The FL engine employs GAT-based farm clustering with DBSCAN outlier exclusion to address non-IID data heterogeneity while maintaining Byzantine fault resilience. Compliance inferences are encoded as R1CS arithmetic circuits (14,240 constraints) and verified on-chain at O(1) cost through ZK-SNARK proofs generated in 1.25 seconds. Evaluated on the Shahhet28121 benchmark dataset across 16 biomarkers, the full system achieves 96.94% global classification accuracy, a 97.7% reduction in per-round communication payload (4.25 KB), and maintains classification accuracy above 90% under 20% Gaussian sensor noise. Ablation experiments confirm that each architectural component contributes independently to system performance. The findings carry implications for green business innovation, sustainable agriculture governance, and the design of trustworthy AI ecosystems in resource-constrained rural contexts.
