arXiv cs.AI· Minghan Yu, Mengyu Dai, Yixin Wen, Haizhao Yang·· 14 小时前AI 评分38
WxCrop：用智能体 AI 从极端天气预测农业损失
From extreme weather to agricultural consequences with agentic AI
AI 导读
WxCrop 是首个通过因果分析检验物理可解释气象指数、并结合气象与农业信息预测作物损失的智能体，可对过去和预报日期给出可靠损失预测。在佛罗里达的飓风、冰冻和干热场景中，其飓风指数识别有记录气旋日的 AUC 达 0.98，留出年份的记录损失复现误差在 2% 以内，独立产量记录验证县级排名相关性为 0.63。用户可按作物类别查询损失占预期季节产值的比例，每个数字均附来源。
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