# Hyperledger Fabric

Intrusion alerts no longer trigger remediation on a single model's word: independent notary agents re-check each verdict and co-sign it on a Hyperledger Fabric ledger only after a 2-of-3 quorum. On the class-balanced CICIDS2017 test set the calibrated ensemble reaches 96.60% accuracy over all decisions, and the certified subset reaches 99.65% accuracy at a 0.04% false-positive rate with 87.3% coverage; on the independently generated GeNIS 2025 corpus the same stack certifies 99.86% of flows at 99.98% accuracy.

Intrusion alerts no longer trigger remediation on a single model's word: independent notary agents re-check each verdict and co-sign it on a Hyperledger Fabric ledger only after a 2-of-3 quorum. On the class-balanced CICIDS2017 test set the calibrated ensemble reaches 96.60% accuracy over all decisions, and the certified subset reaches 99.65% accuracy at a 0.04% false-positive rate with 87.3% coverage; on the independently generated GeNIS 2025 corpus the same stack certifies 99.86% of flows at 99.98% accuracy.

AgriMind-PestGuard couples three parts for paddy pest management: a multi-scale attention CNN that identifies 14 major pest species, a Grad-CAM-HCA module that grounds each decision in entomological concepts, and a Hyperledger Fabric layer that writes detections, pesticide recommendations and compliance records into a tamper-proof ledger. On a curated 58,400-image field dataset it reaches 97.83% classification accuracy, above the strongest baseline EfficientNetV2-S at 96.38%; explanation faithfulness is 0.91 and three entomologists agree with 93.7% of concept attributions. Across 12 paddy farms, field trials report 34.2% less pesticide use and 8.7% higher yield than conventional practice.

AgriMind-PestGuard couples three parts for paddy pest management: a multi-scale attention CNN that identifies 14 major pest species, a Grad-CAM-HCA module that grounds each decision in entomological concepts, and a Hyperledger Fabric layer that writes detections, pesticide recommendations and compliance records into a tamper-proof ledger. On a curated 58,400-image field dataset it reaches 97.83% classification accuracy, above the strongest baseline EfficientNetV2-S at 96.38%; explanation faithfulness is 0.91 and three entomologists agree with 93.7% of concept attributions. Across 12 paddy farms, field trials report 34.2% less pesticide use and 8.7% higher yield than conventional practice.