WxCrop agent predicts crop loss from weather hazards
Original title: From extreme weather to agricultural consequences with agentic AI
Florida tests cover hurricanes, freezes and dry heat, with insurance-calibrated loss models and three reliability checks.
The paper presents WxCrop, an agent that judges agricultural hazards from physically interpretable weather indices examined by causal analysis and combines weather and agricultural data to predict crop loss for past and forecast dates. It is demonstrated in Florida on hurricanes, freezes and dry heat, with a loss model calibrated on insurance records for each hazard. Users request each crop group's loss as a fraction of expected season value, with every number sourced.
Unconfirmed: Reliability figures are the authors' reported checks, not an independent evaluation.
- The hurricane index detects recorded cyclone days with an AUC of 0.98.
- Held-out recorded losses are reproduced to within 2%.
- Independent production records confirm county rankings with rank correlation 0.63.
- The same procedure is described as extending to other hazards and areas.
- Countries
- United States
- AI models
- WxCrop
From the original article
1 min read at arxiv.orgExtreme 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.
By Minghan Yu · arxiv.org
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