[2610.31003] Agri-SCHOLAR: Simulation-Grounded In-Season Rolling Advisories for Agricultural Crops Using LLM-Bounded Optimization

ainst a multi-objective reward spanning yield, resource utilization, and economic feasibility. We conduct a detailed 11-year analysis of the maize crop in two selected locations of South Asia and show that Agri-SCHOLAR consistently outperforms the region’s standard packages of practice and satisfactorily addresses the limitations of existing agricultural advisories.
Subjects: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA)
Cite as: arXiv:2610.31003 [cs.AI]
(or arXiv:2610.31003v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.31003
From: Geetha Charan [view email]

本文转载自：arXiv
原文链接：https://arxiv.org/abs/2610.31003

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