WxCrop：用 agentic AI 从极端天气预测农业损失
From extreme weather to agricultural consequences with agentic AI
AI 导读
arXiv 论文提出 WxCrop，首个基于物理可解释天气指数并结合天气与农业信息预测作物损失的 AI 智能体。团队在佛罗里达州演示其对飓风、霜冻和干热灾害的评估，损失模型基于保险记录校准：飓风指数检测记录气旋日的 AUC 为 0.98，留存年份损失复现误差在 2% 以内，县级排名的秩相关为 0.63。用户可查询各作物组损失占预期季度价值的比例，每个数字均有来源。
正文 · 原文
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.
| Comments: | 34 pages, 4 figures, 6 tables |
| Subjects: | Atmospheric and Oceanic Physics (physics.ao-ph); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.40641 [physics.ao-ph] |
| (or arXiv:2610.40641v1 [physics.ao-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40641 arXiv-issued DOI via DataCite |
Submission history
From: Minghan Yu [view email]
[v1]
Thu, 29 Oct 2026 17:58:53 UTC (7,658 KB)
来源：arXiv cs.AI · arxiv.org