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From extreme weather to agricultural consequences with agentic AI

Paper recorded by Signals 4 on 2026-10-29 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.AI · 人工智能 · first seen 2026-10-30

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 fi

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#64 most recent of 920 cs.AI papers we have recorded · ↑ newer: Luggage re-identification: A study of strategies towards a real-world · ↓ older: Probing Spatial State Representations in Visual Foundation Models
Cite this page: From extreme weather to agricultural consequences with agentic AI: the #64 most recent of 920 cs.AI papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-extreme-weather-to-agricultural-consequences-with-agentic-ai.html
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