# Pest Classification

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.