WxCrop：用 agentic AI 预测极端天气对农作物的实际损失
From extreme weather to agricultural consequences with agentic AI
一个能算出'这场飓风会让橙子亏多少'的 agent，AUC 0.98、误差 2%，做农业气象的可以看看。
arXiv 论文介绍 WxCrop，一个从物理可解释天气指数出发、结合因果分析评估农业灾害风险的 agentic AI。该系统在佛罗里达州针对飓风、霜冻和干热三类灾害进行验证，损失模型基于保险记录校准。三项检验显示其可靠性：飓风指数检测记录中的气旋日 AUC 达 0.98，保留年份的损失复现误差在 2% 以内，县级排名与独立产量记录的秩相关为 0.63。用户可直接询问各作物组的预期损失比例，且每个数字均可溯源。
From extreme weather to agricultural consequences with agentic AI
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.