WxCrop agent turns extreme-weather forecasts into crop-loss estimates
Researchers present WxCrop, an agent that examines physically interpretable weather indices through causal analysis to assess agricultural hazards and predict crop loss for past and forecast dates. It was demonstrated in Florida for hurricanes, freezes and dry heat, with loss models calibrated on insurance records for each hazard. Reported checks include a hurricane index AUC of 0.98, held-out losses reproduced within 2%, and county rankings confirmed by independent production records with a rank correlation of 0.63. Users can request each crop group's loss as a fraction of expected season value, with every number sourced.
It demonstrates agentic AI converting weather forecasts into sourced, crop-specific loss estimates that could support faster agricultural disaster response and risk assessment.
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