English(EN) Agri-SCHOLAR: Simulation-Grounded In-Season Rolling Advisories for Agricultural Crops Using LLM-Bounded Optimization

新AI框架Agri-SCHOLAR提供个性化农业建议

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 09:43

研究人员开发了Agri-SCHOLAR，一个旨在为农民提供个性化生长季农业建议的新框架。该系统通过整合实时天气数据、短期预报和历史天气模式，解决了当前专家驱动建议的局限性。Agri-SCHOLAR将大型语言模型代理与作物模拟器相结合，以优化可操作参数，旨在提高产量、资源利用率和经济可行性。 AI

影响通过向农民提供数据驱动的个性化建议，可以提高农业效率和可持续性。

排序理由该条目是一篇研究论文，详细介绍了用于农业建议的新AI驱动框架。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Y. Narahari · 2026-10-22 09:43

Agri-SCHOLAR: Simulation-Grounded In-Season Rolling Advisories for Agricultural Crops Using LLM-Bounded Optimization

Existing agricultural advisories to farmers suffer from several limitations: (a) remain largely expert-driven, (b) do not adequately account for in-season weather, and (c) do not scale to individual farms. In this paper, our objective is to close these gaps by developing Agri-SCH…