Safety-Aware Cascaded Inference for Crop Damage Assessment with Controlled Error Trade-offs
In picture-based agricultural insurance for smallholder farmers, missed damage detections carry substantially higher cost than false alarms: a farmer who sustained real losses receives no payout, while unnecessary expert review is operationally costly but reversible. Standard multi-class classifiers optimize global accuracy but provide no mechanism to operationalize or control this asymmetric cost structure at inference time. We propose CascadeCropNet, a two-stage cascade architecture calibrated to satisfy a target recall constraint (Rec-Damaged >= 0.95) through threshold selection. A lightweight Sentinel model performs binary health triage; samples exceeding a calibrated damage probabili...
| Authors | José Thiéry Messigbédé Hagbe, Gani Kawsar Gounou, Songbian Karim Zimé |
|---|---|
| Primary category | cs.CV |
| Published | 28 July 2026 |
| Last updated at source | 29 July 2026 |
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