Agentic AI predicts crop losses from extreme weather in Florida
From extreme weather to agricultural consequences with agentic AI
Artificial Intelligence
Summary
Extreme weather like hurricanes, freezes, and heat can damage crops, but it's hard to predict which crops will be affected and by how much. The authors created WxCrop, a smart system that uses weather data and past agricultural records to estimate crop losses accurately. They tested it in Florida using insurance and production records, showing it can closely predict damages from different weather events. This helps farmers and governments respond faster and better to agricultural disasters.
What this means in practice
- •For government disaster teams: Estimate crop damage rapidly after extreme weather events to prioritize emergency response and resource allocation.
- •For agricultural insurance providers: Use combined weather and crop data to improve accuracy in assessing and forecasting losses for underwriting and claims.$Commercial implications: Enables more accurate insurance products that price risk and process claims more efficiently for agricultural clients.
Authors
Minghan Yu, Mengyu Dai, Yixin Wen, Haizhao Yang
Abstract
Extreme weather is forecast days ahead, yet its consequences for agriculture remain hard to predict: a forecast says where a storm will strike, not which crops will suffer or how much is at risk, and the records that could tell span many sources. We present WxCrop, the first agent that judges agricultural hazards from physically interpretable weather indices examined by causal analysis, and the first that combines weather and agricultural information to predict crop loss reliably for past and forecast dates. We demonstrate it in Florida on hurricanes, freezes and dry heat, with a loss model calibrated on insurance records for each, and the same procedure extends to other hazards and areas. Three checks show reliability: the hurricane index detects recorded cyclone days with an AUC of 0.98, recorded losses in held-out years are reproduced to within 2%, and independent production records confirm the county rankings (rank correlation 0.63). Users ask the agent for each crop group's loss as a fraction of expected season value, every number sourced, so that governments, growers and scientists respond to agricultural disasters quickly.